Automated Threat Detection and Deterrence Apparatus

ABSTRACT

An automated threat detection and deterrence apparatus includes an imaging device configured to detect a subject in a subject area, a deterrent component including a directed light deterrent, wherein the directed light deterrent includes a first deterrent mode and a second deterrent mode, the directed light deterrent is configured to perform a first deterrent action on the subject when in the first mode, the directed light deterrent is configured to perform a second deterrent action on the subject when in the second mode, and a processor communicatively connected to the imaging device and the deterrent component, wherein the processor is configured to identify the subject as a function of the detection of the subject, determine a behavior descriptor associated with the subject, select one of the first deterrent mode and the second deterrent mode and command the directed light deterrent to perform an action based on the selection.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of priority of U.S. Provisionalpatent application Ser. No. 63/067,142, filed on Aug. 18, 2020, andtitled “AUTOMATED THREAT DETECTION AND DETERRENCE APPARATUS,” which isincorporated by reference herein in its entirety.

FIELD OF THE INVENTION

The present invention generally relates to the field of security. Inparticular, the present invention is directed to an automated or manualthreat detection and deterrence apparatus.

BACKGROUND

Existing security apparatuses are prone to error and fail adequately todistinguish between genuine threats and harmless bystanders such aschildren. On the other hand, human security is expensive and prone toerror and biases.

SUMMARY OF THE DISCLOSURE

In an aspect, an automated threat detection and deterrence apparatusincludes a detection and sensing component consisting of one or moresensors, including, but not limited to, imaging, acoustic, radar, lidar,time-of-flight, and/or other sensor or sensors, configured to detect andtrack a subject in a subject area, a deterrent component including, oneor more of directed light, sound, chemical, neurostimulator, and/orentanglement deterrent, wherein the deterrent component includes a firstdeterrent mode and one or more secondary deterrent modes, the deterrentcomponent is configured to perform a first deterrent action on thesubject when in the first mode, the deterrent component is configured toperform additional deterrent actions on the subject when insupplementary modes, and the first deterrent action is distinct from theadditional deterrent actions, and a processor communicatively connectedto the detection and sensing component and the deterrent component,wherein the processor is configured to identify the subject as afunction of the detection of the subject, determine a behaviordescriptor, object recognizer, or ruleset associated with the subject,select, as a function of the behavior, object recognizer, or rulesetdescriptor, choose a mode of the first deterrent mode and the additionaldeterrent modes and command the deterrent component or components toperform an action of the first deterrent action and additional deterrentactions as a function of the current mode.

In another aspect a method of automated or manual threat detection anddeterrence includes identifying, by a processor communicativelyconnected to a detection and sensing component, and a deterrentcomponent, a subject as a function of a detection of the subject by thedetection and sensing component, determining, by the processor or thedevice operator, a behavior descriptor, object recognizer, or rulesetassociated with the subject, selecting, by the processor or the deviceoperator, a mode of a first deterrent mode and additional deterrentmodes as a function of the behavior descriptor, object recognizer, orruleset, and commanding, by the processor or device operator, thedeterrent component to perform an action of a first deterrent action andadditional deterrent action or actions as a function of the mode,wherein each deterrent action is distinct from the other deterrentaction or actions.

These and other aspects and features of non-limiting embodiments of thepresent invention will become apparent to those skilled in the art uponreview of the following description of specific non-limiting embodimentsof the invention in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

For the purpose of illustrating the invention, the drawings show aspectsof one or more embodiments of the invention. However, it should beunderstood that the present invention is not limited to the precisearrangements and instrumentalities shown in the drawings, wherein:

FIG. 1 is a block diagram of an embodiment of an automated threatdetection and deterrence apparatus;

FIG. 2 is a block diagram of an exemplary embodiment of an apparatus;

FIG. 3 is a schematic diagram of an embodiment of a subject area;

FIG. 4 is a block diagram of an embodiment of a machine-learning module;

FIG. 5 is a schematic diagram of embodiments of anatomical landmarks;

FIG. 6 is a block diagram of an exemplary embodiment of a neuralnetwork;

FIG. 7 is a block diagram of an exemplary embodiment of a node in aneural network;

FIG. 8 is a block diagram of an embodiment of a decision tree;

FIG. 9 is a schematic diagram of an exemplary embodiment of a finitestate machine;

FIG. 10 is a schematic diagram of an embodiment of a directed lightdeterrent;

FIG. 11 is a schematic diagram illustrating an embodiment of a visualband;

FIGS. 12A-B are schematic diagrams illustrating an embodiment of amultiplexing mirror component;

FIG. 13 is a graph illustrating an exemplary embodiment of color outputfrom a directed light deterrent;

FIG. 14 is a schematic diagram illustrating an exemplary embodiment of adirected audio deterrent;

FIG. 15 is a schematic diagram illustrating an embodiment ofarchitecture of automated threat detection and deterrence apparatus;

FIG. 16 is a schematic diagram illustrating an exemplary embodiment ofan immutable sequential listing;

FIG. 17 is a flow diagram of a method of automated threat detection anddeterrence;

FIG. 18 is a block diagram of an exemplary embodiment of a multimodaldeterrent apparatus with an internal watchdog system;

FIG. 19 is a block diagram illustrating an exemplary embodiment of ananalog control component;

FIG. 20 is a block diagram illustrating an exemplary embodiment of adigital control component;

FIG. 21 is a block diagram of an exemplary embodiment of an autonomoussafety system;

FIG. 22 is a flow diagram illustrating an exemplary method of operatingan autonomous safety system for a deterrent apparatus;

FIG. 23 is block diagram of an exemplary embodiment of a deterrentapparatus;

FIG. 24 is a block diagram of an exemplary embodiment of a system forreceiving feedback input;

FIG. 25 is a process flow diagram illustrating an exemplary embodimentof a method of selecting a deterrent;

FIG. 26 is a block diagram of an exemplary embodiment of a deterrentapparatus;

FIG. 27 is a block diagram of an exemplary embodiment for an ethicaldatabase according to an embodiment of the apparatus;

FIG. 28 is a block diagram of an exemplary embodiment for a deterrentdatabase according to an embodiment of the apparatus;

FIG. 29 is a process flow diagram illustrating an exemplary embodimentof a method of modifying a deterrent;

FIG. 30 is a block diagram of an exemplary embodiment of a deterrentapparatus;

FIG. 31 is a block diagram of an exemplary embodiment for a behavioraccording to an embodiment of the apparatus;

FIG. 32 is a block diagram of an exemplary embodiment for anidentification database according to an embodiment of the apparatus;

FIG. 33 is a process flow diagram illustrating an exemplary embodimentof a method of altering an individual behavior;

FIG. 34 is a block diagram of an exemplary embodiment of an apparatusimportance level;

FIG. 35 is a flow diagram of a method of an apparatus importance level;

FIG. 36 is a block diagram of an exemplary embodiment of a system fortransmitting a notification;

FIG. 37 is a block diagram of an exemplary embodiment of generating abehavior value;

FIG. 38 is a block diagram of an exemplary embodiment of a psychedatabank;

FIG. 39 is a process flow diagram illustrating an exemplary embodimentof a method of transmitting a notification;

FIG. 40 is a block diagram of an embodiment of an automated threatdetection and deterrence apparatus;

FIG. 41 is a schematic diagram of an embodiment of a speechlocalization; and

FIG. 42 is a block diagram of a computing system that can be used toimplement any one or more of the methodologies disclosed herein and anyone or more portions thereof.

The drawings are not necessarily to scale and may be illustrated byphantom lines, diagrammatic representations and fragmentary views. Incertain instances, details that are not necessary for an understandingof the embodiments or that render other details difficult to perceivemay have been omitted.

DETAILED DESCRIPTION

Recent events have revealed that security services provided by humanbeings, from protection of buildings to law enforcement in public, isoften inadequate at best and biased at worst. Recently a federalbuilding was breached by representatives of a political faction, despiteadvance warning that political unrest was likely on the day the breachoccurred. The reasons for this mishap are still a subject ofinvestigation, but apparently were a mixture of divided loyalties,confusion, and uncertainty about how to respond to the burgeoningthreat. Examples of unpredictable violence run amuck abound, leading oneto question whether technology may be used in a more positive way, toinnovate our way out of these attacks. How do we keep safe what issacred? How do we protect the unprotected? Could we respond and protectbefore these horrific events erupt to the point of no return? We havesocial media getting the word out but making the crowd crazy by fuelingthe fire. We can and must do better. Technology can be the solution ifused responsibly. A new paradigm of protection is vitally needed.

In the face of this problem, automated security systems could provide alower-cost alternative free from bias and emotion. However, existingsecurity apparatuses are prone to error and fail adequately todistinguish between genuine threats and harmless bystanders such aschildren, and none thus far has succeeded in performing all thefunctions necessary for a comprehensive security solution.

Embodiments described herein provide for an automated or manuallyassisted security system that reduces error in human judgement and/orprovides for an ability to adequately distinguish between genuinethreatening actions and/or behaviors exhibited by civilians, devices,and/or animals and harmless bystanders. A combination of carefullycalibrated artificial intelligence solutions with efficient protocolsand configurations designed to harness advanced analysis ofpsychological effect produces a system capable of emulating the best ofhuman security response, while substituting for a mind prone to panicand prejudice an inexorable and unflagging system that responds togenuine threats proportionally without bias or overreaction.

Through the automation and calibration of responses in preventing bias,anxiety, or cognitive load a reduced number of overreactions, mistakes,and/or injuries may be achieved that result in reduced emergentsituations that may otherwise occur due to limited human understanding.In embodiments, automation may be designed to escalate from gentlewarnings to more stringent tactics that may be decided faster and withmore precision than human judgement. Automating the escalation allowsfor gradual and/or rapid adjustments of counter measures withoutincorporating emotions and/or other extraneous parameters in determiningthe appropriate counter measure to apply. In an embodiment, thisdisclosure reduces liability for security personnel as a function ofidentifying an undesirable goal and modulating the behavior and/oractions directed towards achieving the undesirable goal. Embodimentsdescribed herein harness behavior and/or situational analysis protocols,combined with human psychology and/or physiology, to select and enactcounter measures that are minimally disruptive to distract an individualof a plurality of individuals, wherein the counter measures may bedesigned to stay within safety limits and to emphasize psychologicalimpact over physical impact. In an embodiment, this disclosure interactswith at least one of five senses that the organisms contain to mitigateunwanted behaviors and/or actions directed towards achieving theundesirable goal. In embodiments, counter measures may include countermeasures that overstimulate at least one of the five senses, such that amotivation to cease the action and/or behavior and leave is established.In other embodiments, counter measures may be persistent, inexorable,immovable, implacable, and the like thereof.

Embodiments disclosed herein may detect entry of persons or animals intosubject areas and respond consistently with determined behaviordescriptors, object recognition, or rulesets using a graduateddeterrence system. Embodiments may use a combination of imaging andother sensors, such as optical cameras, infrared cameras, 3D cameras,multispectral cameras, hyperspectral cameras, polarized cameras,chemical sensors, motion sensors, ranging sensors, light radarcomponent, such as lidar, detection or imaging using radio frequenciescomponent, such as radar, terahertz or millimeter wave imagers, seismicsensors, magnetic sensors, weight/mass sensors, ionizing radiationsensors, and/or acoustical sensors, to accurately recognize andspatially determine entrance into the subject area, to distinguishbetween known or whitelisted persons, children, animals, and potentialthreats. Embodiments may further distinguish casual or accidentalintruders from those with more purposeful or malicious intent and maycalibrate responses according both to detected behavior, imminence ofthreat, or other rulesets. Deterrent responses may be calibrated todetected behavior descriptors and rulesets so as to generate a graduatedresponse that can escalate from warnings to irritating or off-puttingresponses or further to incapacitating responses as needed to achievesecurity objectives with a minimal harm to the intended target.

Embodiments as described in this disclosure may provide an advantage offorce multiplication. In scenarios where multiple persons such assharpshooters would be required to engage a potential assailant at 100yards of separation with near total confidence, use of embodimentsdescribed in this disclosure may enable engagement of multipleassailants, at a comparable distance, by a single device which may beoperating autonomously or with minimal human input. Such engagement,moreover, may be calibrated to avoid harming potential assailants and todeploy no more energy or force for interdiction than is necessary.

Embodiments described herein may further provide a benefit of distanceinterdiction. In many scenarios, an ability to engaged and/or dissuade apotential assailant and/or malefactor before such a person has a chanceto approach near to a target person or property may be highlyadvantageous; for instance, a would-be school shooter or home invadermay be much more difficult to deter once they have entered the school orhome they are targeting. Embodiments of apparatuses described herein maybe able to detect and interdict potentially problematic persons atdistances of 100 yards or more, preventing close engagement frombecoming an issue.

Embodiments described herein may be able to create an impression in apotential assailant and/or other subject of potential interdiction thatthey are interacting with a live human such as security personnel,rather than with an automated apparatus as described in this disclosure.This may be enabled by intelligent deployment of deterrents, forinstance avoiding attempts to target a user when they have taken coveror otherwise made themselves unavailable for targeting. Imitation of alive human may be further augmented using text-to-speech technology toimitate a voice and/or diction of a live person.

Some embodiments described in this disclosure may present a technologythat is far safer than an attack dog or even a Taser, that can beunleashed hundreds of feet away from the troubled hotspot. Embodimentsmay provide an Active Denial Area solution providing layers of activedenial countermeasures without causing any long-term harm. Technologymay serve as an escalating barrier to inform and warn an individual thatthey are about to violate a secured property and persuade them to leavebefore an irreversible outcome would make it to headline news as is toooften the case involving flying bullets. Embodiments described hereinmay be able to stop an offender dead in their tracks faster than theblink of an eye, without any bloodshed. Some embodiments may be able tosimultaneously intercept and interdict 30 offenders at 300 feet in 3tenths of a second all without any long-term harm to the offenders.

Embodiments described in this disclosure may have the potential toreplace or significantly reduce costs of human assets, minimizeunexpected outcomes and stand guard endlessly. If a situation requireshuman intervention embodiments may serve as a force multiplier.Embodiments of apparatus may, at best, prevent access to a secured areaand at worst, delay and confuse a perpetrator sufficiently as to allowthe authorities to respond and secure the situation.

In some embodiments, apparatus may perform a graduated response,initiating with hailing an intruder, followed by informing them and ifnecessary, interdicting to repel them. Apparatus may follow a subject'severy movement even while they are running and target an ever-increasingbarrage of countermeasures. Apparatus may maintain an intensifying stateof persuasion and dissuasion until an offender and/or violator is forcedto vacate a secured area.

Some embodiments of apparatus may be capable of detection andinterdiction at a 100 m range. Apparatus may operate day and night,indoors or outdoors. Apparatus may ultimately acquire, track and targetup to 30 individuals simultaneously providing a 500 FOV for the computervision (CV), laser and steerable speaker array.

Referring now to FIG. 1 , an exemplary embodiment of an automated threatdetection and deterrence apparatus 100 is illustrated. Apparatus 100includes an imaging device 104 configured to detect a subject 308 in asubject area. Imaging device 104 may include an optical camera 108. An“optical camera,” as used in this disclosure, is a device that generatesstill, video, and/or event-based images by capturing senseselectromagnetic radiation in the visible spectrum, having wavelengthsbetween approximately 380 nm and 740 nm, which radiation in this rangemay be referred to for the purposes of this disclosure as “visiblelight,” wavelengths approximately between 740 nm and 1,100 nm, whichradiation in this range may be referred to for the purposes of thisdisclosure as “near-infrared light” or “NIR,” and wavelengthsapproximately between 300 nm and 380 nm, which radiation in this rangemay be referred to for the purposes of this disclosure as “ultravioletlight” or “UV”. Optical camera 108 may include a plurality of opticaldetectors, visible photodetectors, or photodetectors, where an “opticaldetector,” “visible photodetector,” or “photodetector” is defined as anelectronic device that alters any parameter of an electronic circuitwhen contacted by visible, UV, and/or NIR light. Optical detectors mayinclude, without limitation, charge-coupled devices (CCD), photodiodes,avalanche photodiodes (APDs), silicon photo-multipliers (SiPMs),complementary metal-oxide-semiconductor (CMOS), scientific CMOS (sCMOS),micro-channel plates (MCPs), micro-channel plate photomultiplier tubes(MCP-PMTs), single photon avalanche diode (SPAD), Electron BombardedActive Pixel Sensor (EBAPS), quanta image sensor (QIS), spatial phaseimagers (SPI), quantum dot cameras, image intensification tubes,photovoltaic imagers, optical flow sensors and/or imagers,photoresistors and/or photosensitive or photon-detecting circuitelements, semiconductors and/or transducers. APDs, as used herein, arediodes (e.g. without limitation p-n, p-i-n, and others) reverse biasedsuch that a single photon generated carrier can trigger a short,temporary “avalanche” of photocurrent on the order of milliamps or morecaused by electrons being accelerated through a high field region of thediode and impact ionizing covalent bonds in the bulk material, these inturn triggering greater impact ionization of electron-hole pairs. APDsmay provide a built-in stage of gain through avalanche multiplication.When a reverse bias is less than breakdown voltage, a gain of an APD maybe approximately linear. For silicon APDs this gain may be on the orderof 10-100. The material of the APD may contribute to gains.

Still referring to FIG. 1 , individual photodetectors in optical camera108 may be sensitive to specific wavelengths of light, for instance byuse of optical filters to exclude such wavelengths; for instance, andwithout limitation, some photodetectors may be sensitive to blue light,defined as light having a wavelength of approximately 420 nm to 480 nm,some may be sensitive to green light, defined as light having awavelength of approximately 495 nm to 545 nm, and some may be sensitiveto red light, defined as light having a wavelength of approximately 620nm to 750 nm. Combinations of photodetectors specifically sensitive tored, green, and blue wavelengths may correspond to wavelengthsensitivity of human retinal cone cells, which detect light in similarfrequency ranges. Photodetectors may be grouped into a three-dimensionalarray of pixels, each pixel including a red photodetector, a bluephotodetector, and a green photodetector. Pixels may be small enough tofit millions into a rectangular array less than an inch across. Opticalcamera may include one or more reflective, diffractive, refractive,and/or adaptive components that focus incident light ontophotodetectors.

With continued reference to FIG. 1 , imaging device 104 may include aninfrared camera 112. An “infrared camera,” as used in this disclosure,is a camera that detects electromagnetic radiation in the infraredspectrum, defined as a spectrum of electromagnetic radiation havingwavelengths between approximately 740 nm and 14.0 μm, which radiation inthis range may be generally referred to for the purposes of thisdisclosure as “infrared light,”. As non-limiting examples, infraredcamera 112 may detect light in the 1.0 to 3.0 μm range, which radiationin this range may be referred to for the purposes of this disclosure as“shortwave infrared light” or “SWIR,” may detect light in the 3.0 to 5.0μm range, which radiation in this range may be referred to for thepurposes of this disclosure as “midwave infrared light” or “MWIR,” ormay detect light in the 8.0 to 14.0 μm range, which radiation in thisrange may be referred to for the purposes of this disclosure as“longwave infrared light” or “LWIR.” Infrared camera 112 may include aplurality of infrared detectors or infrared photodetectors, where an“infrared detector” or “infrared photodetector” is defined as anelectronic device that device that alters any parameter of an electroniccircuit when contacted by infrared light. Infrared detectors mayinclude, without limitation, silicon photodiodes doped to detectinfrared light, strained-layer super lattice (SLS) photodetectors,quantum well infrared photodetectors (QWIP), amorphous silicon (αSi)photodetectors, Vanadium Oxide (VOx) microbolometers, Barium StrontiumTitanate (BST), thermopile array detector, pyroelectric infrareddetectors, detectors constructed from narrow bandgap detector materialsfrom the III-V elemental group, and/or other infrared photoelectric,photovoltaic and/or microbolometer based detectors. A “microbolometer”is defined for the purposes of this disclosure as a specific type ofbolometer used as a detector in a LWIR camera, also known as a “thermalcamera.” Microbolometer may detect infrared light when infraredradiation with wavelengths between 7.5-14 μm strikes detector material,heating it, and thus changing its electrical resistance. Alternativelyor additionally, an infrared camera may consist of a single, sensitivelarge pixel, such as a passive infrared (PIR) sensor or other singleelement infrared sensitive detector.

Continuing to refer to FIG. 1 , infrared camera 112 may use a separateaperture and/or focal plane from optical camera 108, and/or may beintegrated together with optical camera 108. There may be a plurality ofoptical cameras 108 and/or a plurality of infrared cameras 112, forinstance with different angles, magnifications, and/or fields-of-view ofperspective on a subject area. Alternatively or additionally, two ormore apparatuses coordinated using a communication network, as describedin further detail below, may be combined to generate two or more imagesfrom varying perspectives to aid in multi-dimensional imaging and/oranalysis.

Still referring to FIG. 1 , imaging device 104 may include a light radarcomponent 116. A “light radar component,” as defined in this disclosure,is an active imaging source that transmits light toward an object orfield of interest and detects back-scattered, absorbed, or reflectedlight, measuring time of flight (ToF), interferometry, and/or phase ofsuch back-scattered and/or reflected light to compute distances to,velocities, and/or accelerations of objects at points from whichback-scatter and/or reflection occurred. A light radar component 116 mayinclude, without limitation, LIDAR or related mechanisms. In someembodiments, active light source may include a high-intensity lightsource, which may be focused, collimated, and/or coherent, enabling fineplacement within a coordinate system, for instance as described below,of points in a field of view and/or at an object of interests at whichtransmitted light is scattered and/or reflected; active light source mayinclude without limitation a laser such as an edge-emitting laser diode(EELD), a high-intensity light-emitting diode, a high-intensity “super”light-emitting diode consisting of a single or plurality of lasersand/or phosphor material, super-luminescent light-emitting diode, and/orvertical-cavity surface-emitting laser (VCSEL) or EELD or VCSEL array. Alaser may include a laser diode, which may be electrically pumped;alternatively or additionally, laser may be pumped optically. Activelight source may transmit light in a narrow band of wavelengths; forinstance, active light source may transmit light that is substantiallymonochromatic. In embodiment, light transmitted by active light sourcemay pass through a dichroic filter, polarizing filter, diffractiveoptical element, meta-material, spatial light modulator (SLM), orsimilar optical element, which may further narrow a transmittedwavelength range, modify the shape or pattern, modify the polarization,modify the wavefront, or affect other properties of the active lightsource. Wavelength of light may be outside the range of visible light;for instance, and without limitation, wavelength may be in the infraredrange as described above. Light radar component may include a “flashlidar” component, mechanical or non-mechanical beam steering, lightpatterns, and/or computational imaging methods, such as plenoptic orother multi-aperture embodiments.

Still referring to FIG. 1 , light radar component 116 may include one ormore optical elements for focusing, collimating, and/or transmittinglight emitted by light source. One or more optical elements may includea focal optical suite, which may bend light to converge to a real and/orvirtual focal point. Focal optical suite may be reflective, diffractive,adaptive, and/or refractive; for instance, and without limitation, focaloptical suite may include two or more lenses spaced apart, where spacingbetween lenses may be varied to modify a focal length of transmittedlight. Dispersal and/or focus of transmitted light may be controlledusing electronically focused lens assembly, where adjustment ofdistances or alignment between lenses may be electrically ormechanically actuated. Intensity or temporal composition of transmittedlight may be variable as well, where variation may be modified usingvaried voltage levels, electrical current levels, waveforms, multiplepulses, duty cycles, pulse widths, passive or active optical elements,such as Q-switches, acoustical optical tunable filters (AOTF), and/orspatial light modulators (SLM). Electrical voltage and current levels,and durations to light source may be regulated analog or digitally byoutput of a logic circuit and/or processor 136 to a digital to analogconverter, an on/off cycle to a transistor such as a power field-effecttransistor, pulse width modulation provided natively by a processor, orthe like. In an embodiment, intensity and/or focus may default tominimally harmful settings, permitting allowing ToF ranging or the liketo determine a distance to a nearest subject 308 in a subject space,after which focal length and intensity may be set as permitted bystandards of safe exposure. Alternatively or additionally, where awavelength of light source is invisible and non-ionizing, intensitylevels may be intrinsically safe across an operational range of lightsource.

With continued reference to FIG. 1 , light radar component 116 mayinclude one or more optical elements may include one or more reflective,diffractive, refractive, and/or metamaterial scanning elements fordirecting a beam from light source across a space to be scanned. As anon-limiting example, one or more optical elements may make use of amirror galvanometer to direct a beam in scanning pattern. Scanning maybe performed across two dimensions, using one or more optical elementsand methods of directing individually or in combination for “beamsteering,” including but not limited to, two flat or polygonal mirrorsthat may be driven by a galvanometer, electric motors, micro-electromachined systems (MEMS) or micro-optical electro machined systems(MOEMS) microscanner devices, piezoelectric actuated devices,magnetostrictive actuated devices, liquid, polymer, or othermechanically deformable devices, fast steering mirrors (FSM), Risleyprisms, decentered macro-optical elements and micro-lens arrays, blazedgrating optical elements, MOEMS or MEMS combined with macro-opticalelements, phased arrays, electronically steered arrays, spatial lightmodulators (SLM), holographic optical elements, laser intra-cavity beamsteering, and/or metamaterial surfaces or structures. A beam mayalternatively or additionally be aimed and/or focused in three or moredimensions, for instance by using a servo-controlled lens system, whichmay be referred to without limitation as a “focus shifter,” “beamexpander,” or “z-shifter.” Intensity of emitted light may alternativelyor additionally be used. Mirrors perform a periodic motion using, forinstance, rotating polygonal mirrors and/or a freely addressable motion,as in servo-controlled galvanometer scanners. Control of scanning motionmay be effected via a rotary encoder and/or control electronicsproviding electric current to a motor or galvanometer controlling mirrorangle. Electrical current may be varied using a servo controller digitalto analog converter such as a DAC81516 as produced by Texas Instruments,Inc. of Dallas, Texas. Alternatively or additionally, the beam may beaimed and/or focused using a “non-mechanical” beam steering method, suchas spatial light modulators (SLM) by adjusting the liquid crystal matrixthat makes up the pixels of such device using digital or analog drivecontrollers to modify the angles of alignment of the liquid crystals asto make dynamic diffractive patterns to provide beam shaping and aiming.A laser's wavefront passing through the liquid crystal matrix isaffected by the calculated diffractive patterns to provide bothdeflection of the beam for aiming, and an optical function for focusingor shaping the profile of the beam.

Still referring to FIG. 1 , light radar component 116 may include atleast a visible or infrared photodetector, which may be implementedusing any suitable visible or infrared photodetector and/or plurality ofvisible or infrared photodetectors as described above. For instance, andwithout limitation, at least a photodetector may include a detectorarray, such as a detector array suitable for use in an optical orinfrared camera 112 as described above. Detectors in detector array maybe sensitive specifically to a narrow band of wavelengths transmitted bylight source, and/or may be sensitive to a range of wavelengths thatincludes the band transmitted by the light source. Detectors may bedesigned to react quickly to initial detection of photons, for instancethrough use of APDs or other highly sensitive detectors. Light radarcomponent 116 may include one or more receptive optical elements, whichmay include collimating and/or focusing mirrors and/or lenses. One ormore receptive optical elements may include filters such as withoutlimitation dichroic, polarization, bandpass, notch, and/or other opticalfilters, which may act to screen out light that is not transmitted bylight source; this may drastically increase signal to noise ratio, andmay further act to prevent disruption of light radar component 116 by adirected light deterrent as described in further detail below.Alternatively or additionally, signal to noise ratio can be increasedfor the light radar component 116 by modulating the signal such that thetiming or frequency shifting of the transmitted beam is recognized bythe detection circuit over the constant background ambient signal bysubtracting the background from the signal.

Still referring to FIG. 1 , one or more optical, electronic, and/ordigital filters may be employed at optical sensors to limit effects ofnoise. For instance, and without limitation, a sensor attempting todetect a particular wavelength, such as a sensor used in light-radardevices as described in this disclosure, may have an optical,electrical, and/or digital bandpass filter designed to permit passageand/or detection of the wavelength of interest. Such a notch filter mayprevent “blooming” energy in background. For instance, where a camera isconfigured to detect a reflection of 850 nm light, or any otherwavelength described in this disclosure as suitable for use with lightradar, a filter may exclude and/or drastically attenuate substantiallyall other wavelengths. As a further non-limiting example, light used inlight radar and/or other detection processes and/or components may beset at a wavelength for which natural light sources are generallyattenuated. For instance, an absorption spectrum of water may be higheraround 850-900 nm. Any wavelength from the visible spectrum up to 2microns may alternatively or additionally be used. Thus, the atmospheremay tend to block solar radiation at such a spectrum; generally,frequencies may be used that are in low-transmissivity bands forsunlight and/or other potential sources of electromagnetic noise, toreduce noise therefrom. As a further example, ultraviolet frequenciesmay be largely attenuated from solar radiation, enabling use thereofwith relatively low solar interference. In an embodiment, differentwavelengths may be used under different circumstances to maximize signalto noise ratio; for instance, different wavelengths may be employed forlight radar at night than during the day.

In an embodiment, and further referring to FIG. 1 , light radarcomponent 116 may perform ToF calculation, by firing pulses of light andmeasuring time required for a backscattered and/or reflected pulse toreturn. Time may be measured using an oscillator-based clock, where afaster clock signal may enable more accurate measure of the time a pulsetakes to return to detector. ToF may alternatively or additionally bemeasured using an amplitude modulated continuous wave (AMCW) technique,whereby light is emitted continuously from light source with a varyingamplitude, and a phase of returning detected light is compared to aphase of transmitted light. For instance, light source may cast amodulated illumination in a near-infrared (NIR) or short-wave infrared(SWIR) spectrum onto a scene, and then record an indirect measurement ofthe time it takes the light to travel from the light source to a portionof the scene and back using phase and/or interferometric comparison;phase comparison may, without limitation, be performed by comparing aphase of returning light to a phase of a reference beam separated fromtransmitted light using a beam splitter.

Still referring to FIG. 1 , ToF may be used to measure a distance fromlight radar component 116 to a point from which light is scattered; thismay be used, without limitation, to detect distance to an object such asa subject 308 into a subject area. Distance may be computed using asingle reading of ToF, by averaging two or more ToF readings, and/ormeasuring multiple returns to reduce false readings from clutter. ToFmay be used to detect edges of objects such as a subject 308, a portionof anatomy of a subject 308, an object held by a subject 308, or thelike. For instance, and without limitation, an edge may be detected bycomparison of ToF at detected points to nearby and/or adjacent ToFreadings, where a border separating a region of relatively smaller ToFreadings from a region of relatively more distant ToF readings mayidentify an edge. As a non-limiting example, such a border may define anoutline of a person with a wall or other object behind the person. ToFmay be used to generate an image, for instance by repeatedly capturingreadings of ToF to different portions of an object; a three-dimensionalsurface contour of the object, such as facial features, details of anobject a person is holding, or the like, may be rendered using the ToFdata. ToF measurements may be processed to generate a depth map or pointcloud, defined for the purposes of this disclosure as a set ofZ-coordinate values for every pixel of the image, which may be measuredin units of millimeters, micrometers, or the like. Depth map data may becombined with other imaging data; for instance, intensity or phasevalues of pixels in an infrared reading may be measured as proportionalto an amount of light returned from a scene.

In an embodiment, and still referring to FIG. 1 , light radar component116 and/or imaging component may include and/or communicate with asurveillance component. As used in this disclosure a “surveillancecomponent” is a device and/or component capable of tracking and/ormonitoring an individual of a plurality of individuals. For example, andwithout limitation, surveillance component may track an individual in acrowd, wherein the individual may be monitored in addition to monitoringthe crowd. As a further non-limiting example, surveillance component maytrack one or more behaviors, wherein behaviors are described below indetail.

Continuing to refer to FIG. 1 , imaging device 104 may include anultrasound device 120. An ultrasound device, as used in this disclosure,is a device that emits sound having frequencies in excess of 20kilohertz. An ultrasound device may measure distances to objects such asboundaries of a subject area, items in the subject area, and/or subjectstherein using time of flight measurements, by emitting sounds andmeasuring time until detection of returning echoes; range-finding usingsuch measurements may be described as “sonar.” Ultrasound device 120 maybe used to measure distances in combination with and/or in lieu ofdistance finding using stereoscopic camera and/or light radar ToFtechniques. In an embodiment, ultrasound device 120 may generate images,which may be combined with and/or used to supplement images taken usingoptical camera 108, infrared camera, 112, light radar component 116, orany combination thereof.

Now referring to FIG. 2 , an exemplary embodiment 200 of apparatus 100is illustrated. Imaging device 104 may include a 3D detector 204. 3Ddetector 204 may include, without limitation 3D cameras, sensors, orcomputational methods. 3D detector 204 may include, without limitation,optical detectors, visible photodetectors, photodetectors, infrareddetectors, laser range finders, and/or light or radio frequency radarcomponents. Components of 3D detector 204 may implement passive methods,defined as methods that do not require admission of any electromagneticand/or acoustical energy, to determine a three-dimensional structure ofa real-world scene. Components of 3D detector 204 may implement activemethods, defined as methods that require emission of electromagneticand/or acoustical energy, to determine a three-dimensional structure ofa real-world scene. 3D detector 204 may process the data gathered usingmathematical, temporal, and/or logical algorithms or electronics tocreate a three or more-dimensional representation of a real-world scene;representation may provide relative spatial coordinates from 3D detector204 field of view and/or field of regard. “Field of view” is defined forthe purposes of this disclosure as an instantaneous image that a sensoris capable of capturing, while a “field of regard” is a combination ofmultiple fields of view and/or the possible range of fields of view asensor can capture based on changing parameters of the sensor, such as,and without limitation, changing a sensor's location, orientation,direction, rotation, roll, inclination, magnification, etc. throughmotion, optics, physical manipulation, and/or other methods to changeits ability to image or detect a scene.

Still referring to FIG. 2 , 3D detector 204 can use methods fordetermining a 3D makeup of a scene, for instance, and withoutlimitation, using multiple cameras for stereo imaging, photogrammetry,time of flight methods, structured light, shape from motion, shape frompolarimetry, lidar, radar, sonar, synthetic aperture imaging, multipledisparate apertures, gated imaging, single or multiple pixel rangefinding, artificial intelligence methods, event, and/or other rangingmethods. A resulting output may include and/or be included in anaccessible data format providing positional and/or compositionalinformation of some or all of the objects in a scene that represent areal world in a system of coordinates, for instance, and withoutlimitation, Euclidean, Cartesian, and/or polar coordinates. Dataincluded in output may also contain additional information, such aswithout limitation an object's electromagnetic or acousticalreflectivity, electromagnetic or acoustical absorption, material makeup,obscuration, occlusion, electromagnetic or acoustical emissions, opticalcharacteristics, acoustical characteristics, and/or shapecharacteristics.

Still referring to FIG. 2 , imaging device 104 may include componentsfor detection or imaging using radio frequencies, such withoutlimitation a radar component 208 and/or a wavelength detector 212, wherea wavelength detector may include a millimeter wave sensor or imagerand/or terahertz sensor or imager. As non-limiting examples, “radiofrequency,” “radio frequencies,” and/or “RF” sensors or imagers maydetect electromagnetic radiation in the approximate range of 30 hertz(Hz) to 400 gigahertz (GHz) (wavelengths of approximately 10,000 km to 1mm, respectively). “Millimeter wave,” “MMW,” or “microwave,” as used inthis disclosure, is defined as a spectrum of electromagnetic radiationhaving frequencies between approximately 3 GHz to 400 GHz (wavelengthsof approximately 100 mm to 1 mm, respectively). “Terahertz” or “THz,” asused in this disclosure, is defined as a spectrum of electromagneticradiation having frequencies between approximately 0.1 THz to 22 THz(wavelengths of approximately 3 mm to 14 μm, respectively). Such devicesmay use RF sources, in either an active embodiment, where a deviceprovides its own RF emissions for detection, or in a passive embodiment,where a device relies on external RF sources to provide energy fordetection. RF sensors and imagers may include a receiver and/ortransmitter (if active). A receiver may include a front-end with anantenna and/or optics used to collect, direct, and/or manipulateincoming RF energy into a detector of a receiving subsystem. An antennaand/or one or more elements of optics may include a plurality ofconfigurations, materials, meta-materials, geometries, structures,and/or methods to specify, enhance, reject, amplify, focus, filter,provide directionally, and/or further modify frequencies of the RFspectrum for an RF detector to receive. An “RF detector,” as used inthis disclosure, may include a plurality of “RF receivers,” “RFdetectors,” “RF sensors,” or “RF focal plane arrays” that are defined asan electronic device or structure that alters any parameter of anelectronic circuit when contacted by radio frequencies. RF detectors mayinclude, without limitation, resistor-capacitor (RC) resonant circuits,inductor-capacitor (LC) resonant circuit, resistor-inductor-capacitor(RLC) resonant circuit, resonant RF photodetectors (RRFP), tunedmicro-engineered meta-structures, Schottky diodes, Schottky receivers,microbolometers, antenna/microbolometer structures, RF sensitivephotodiodes, resonant tunneling diode (RTD), pair braking detectors, hotelectron mixers and/or field-effect transistor detectors. After a signalis converted by a RF detector, it may, as an example, be furtherprocessed by a low noise amplifier to improve signal to noise ratio ofreceived energy and then converted to a digital signal for processingthat may include, without limitation, determination of range to anobject, determination of chemical makeup of an object, detection and/oridentification of hidden objects, determination of speed of an object,creation of an image an object and/or scene in the respective frequency,and/or change detection.

Still referring to FIG. 2 , imaging device 104 may include a polarizedcamera 216, multispectral camera 220, and/or hyperspectral camera 224. A“polarized camera,” as defined in this disclosure, is a camera or sensorthat uses methods or filters to determine, measure, and/or analyzeStokes parameters of an object. “Stokes parameters” or “Stokes vectors,”as defined in this disclosure, are mathematical representations ordescriptions used to characterize the radiance (intensity) or flux andstate of polarization of a beam of electromagnetic radiation. Stokesparameters were first introduced by G. C. Stokes in 1852 in thepublication “On the composition and resolution of streams of polarizedlight from different sources.” These are usually represented as sixgeneral values of 0°, 45°, 90°, 135°, right hand circular, and left-handcircular. A polarized camera may be able to discern between Stokesvectors and provide them for further processing, analysis, viewing, orstorage. A “multispectral camera,” as defined in this disclosure, is animaging system that can concurrently detect and image several spectra ofelectromagnetic, acoustic, and/or other modalities, for instance, andwithout limitation, visible light, NIR light, SWIR light, MWIR light,LWIR light, portions thereof, and acoustic energy and/orradiofrequencies, instead of a single spectrum. In an embodiment, amultispectral camera may include several types of visible photodetectorsand infrared photodetectors collocated with each other on a singledetecting sensor. In another embodiment, a multispectral camera mayinclude a plurality of visible cameras and/or infrared cameras whoseimagery is combined into a single output for analysis. A “hyperspectralcamera,” as defined in this disclosure, is an imaging system that candetect and discriminate specific frequencies, wavelengths, or wavenumbers of electromagnetic, acoustic, radiological, or other energyemissions, within a spectral band of a sensor or camera. In anembodiment, a hyperspectral visible camera may be able to discern, froma spatial image, highly detailed wavelength reflection per pixel of areal-world scene. For instance, but without limitation, this may includesplitting a typical visible camera detection spectrum of visible light(380 nm-740 nm) and NIR light (740 nm-1,100 nm) into 16 ranges, about 45nm per “band,” that results in a spatial image with 16 channelsconsisting of image data with reflected electromagnetic energy rangingfrom 380 nm-425 nm, 425 nm-470 nm, and so on, continuing to 1,055nm-1,100 nm. This data may then be exploited to determine specialcharacteristics of the scene, for instance, and without limitation, perpixel information of chemical makeup, material properties, organic orinorganic, spectroscopy, and/or other characteristics of the object thatwas imaged.

With continued reference to FIG. 2 , imaging device 104 may include bothhigh-resolution and low-resolution visual sensors. In an embodiment,imaging device 104 and/or one or more processors, computing devices,logic circuits, or the like in and/or communicating with apparatus 100may select low-resolution or high-resolution sensors as a function ofone or more determinations based on accuracy, speed, and resourceallocation. For instance, and without limitation, use of alow-resolution sensor may require less bandwidth; as an example, acamera or other imaging device that captures fewer pixels and/or voxelsmay require fewer parallel wires and/or fewer bits of data pertwo-dimensional or three-dimensional image and/or frame, and thusrequire a lower volume of data per two-dimensional or three-dimensionalimage and/or frame. Where data transmission and/or exchange isserialized anywhere within apparatus 100, broken into “words” forregister, cache, and/or memory retrieval and/or for computation usingarithmetic and logic units (ALUs), floating-point units (FPU)s, or thelike, time spent on serialization and/or performance of multipleinstructions may be reduced for lower bandwidths, improving potentialprocessing speeds. Similarly, a multitasking processor or circuitelement may be able to free up more resources for additional sensors ifa given sensor is lower in resolution. Alternatively, where processingspeeds at higher bandwidths are still sufficient and/or greaterparallelization and/or multithreading of hardware and/or softwareroutines, bundling of serial lines, or the like permit and/or enablehigher processing speeds at high resolution, a high-resolution sensormay provide greater detail and/or more information without sacrificingperformance. In some embodiments, and without limitation, apparatus 100may include a low-resolution sensor with a high level of reliabilityand/or confidence, which may be used to locate and/or aim ahigher-resolution sensor to obtain details; as a non-limiting example, alow-resolution sensor may be used to find a general outline of aperson's head or face, to detect eyes using gross features, retinalreflection, or the like, and once the location of the head or face isidentified a high-resolution camera, LIDAR device, or the like may beused to obtain more detailed visual data from the face to permit, forinstance, image classifiers that identify individuals, behaviorclassifiers that determine expressions, or the like to use thehigh-resolution data as inputs. Alternatively or additionally,high-resolution sensors may be used to identify a feature of interestwhich thereafter may be tracked using a low-resolution sensor; forinstance a high-resolution sensor may be used to identify and determinefacial recognition and/or expression data concerning an individual, aswell as one or more landmarks for low-resolution tracking, the latter ofwhich may be provided to a low-resolution sensor and/or a circuitcontrolling a low-resolution sensor, which may then be used to track theidentified feature using the landmarks. For instance, edges of a visualband as described below may be identified using an image classifier anda high-resolution sensor, and thereafter a low-resolution sensor may beused to track the edges and thus the visual band. Each of theseprocesses may be combined and/or repeated; for instance, a face may beinitially identified using a low-resolution sensor and trackedthereafter therewith, while a high-resolution sensor may periodicallytake snapshots or brief captures of additional data to maintain andconfirm accuracy of tracking, update facial expression data, and thelike.

More generally, and continuing to refer to FIG. 2 , apparatus 100 maycombine multiple sensors together to detect subject and/or make anydeterminations as described in this disclosure. Combinations may includeany combinations described below.

Still referring to FIG. 2 , one or more sensors may include an ionizingradiation sensor 228, defined as a sensor that detects ionizingradiation, such as alpha, beta, gamma, and/or neutron radiation, fromradioactive material that is in solid, liquid, or gas form. An ionizingradiation sensor 228 may include, without limitation, a Geiger-Muellertube, silicon photodetector, scintillation material (such as organiccrystals, organic liquids, plastic scintillators, inorganic crystals,gaseous scintillators, glasses, solution-based perovskitescintillators), scintillator coupled with a photomultiplier, bubbledetectors, semiconductor based detectors (such as cadmium zinc telluride(CZT), boron nitride (BN), gallium nitride (GaN), Gadolinium Nitride(GdN)), and/or ionizing radiation imaging techniques, for instance, andwithout limitation, imaging ionized gas florescence due to the emittedradiation field. One or more ionizing radiation sensors 228 may beconfigured to detect radioactive material in possession of a subject orthat is in the vicinity of subject area. One or more ionizing radiationsensors 228 may include one or more directional sensors or imagers thatenable spatial location of a radiation material. One or more ionizingradiation sensors 228 may detect ionizing radiation, direction ofemanation of ionizing radiation, and the like and convert such detectedsignals and/or directional data into electrical signals, which may beprocessed further by apparatus 100. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of variousforms of ionizing radiation sensors 228 that may be deployed, as well asvarious ionizing radiation phenomenon that ionizing radiation sensors228 may be used to detect.

Still referring to FIG. 2 , one or more additional sensors may include aseismic sensor 232, defined as a sensor that detects vibrationstransmitted through the ground, building, or other object apparatus 100may be attached to and/or in communication with, or through a remotesensor in the vicinity of the apparatus 100. At least a seismic sensor232 may include, without limitation, a MEMS sensor, infrasound detector,and/or counter balanced weight. One or more seismic sensors may beconfigured to detect vibrations made by subjects in a subject area, suchas people, animals, and/or machines. One or more seismic sensor mayinclude one or more directional seismic sensors, defined as seismicsensors that are exclusively or preferentially sensitive to vibrationsemanated from a particular direction within subject area or vicinity; adirectional seismic sensor may include arrays of seismic sensors. One ormore seismic sensors 232 may detect vibrations, direction of emanationof vibrations, or the like and convert such detected signals and/ordirectional data into electrical signals, which may be processed furtherby apparatus 100 as described in further detail below.

Still referring to FIG. 2 , one or more additional sensors may include amass sensor 236, defined as a sensor that detects changes in mass orweight in a specific area and sends resulting information to apparatus100. At least a mass sensor 236 may include without limitation, a MEMSsensor, stress gauge, or and/or spring scale. One or more mass sensorsmay be configured to detect mass or weight changes made by subjects in asubject area, such as people, animals, and/or machines. One or more massor weight sensors 236 may detect changes in mass or weight, or the likeand convert such detected signals into electrical signals, which may beprocessed further by apparatus 100 as described in further detail below.

Still referring to FIG. 2 , one or more additional sensors may include amagnetic sensor 240, defined as a sensor that detects changes inmagnetic fields, such as from metal objects that are concealed or notoriginally present in subject area. At least a magnetic sensor 240 mayinclude without limitation, a MEMS sensor or a sensor including one ormore wire coils. One or more magnetic sensors may be configured todetect changes in magnetic fields by subjects or objects in a subjectarea, such as weapons, machines, electronic devices, and/or otherobjects consisting of metal. Magnetic sensors may be active or passive,where active sensors may use modulated signals emitted from atransmitter, such as a wire coil, to induce electrical eddy currents inmetal objects; these induced currents may then be received by a similarcoil to detect and possibly discriminate metals, both ferrous andnon-ferrous. Passive magnetic sensors may sense changes to a baselinemagnetic field that they are in and detect changes in the field byinduced electrical current in coils or Hall effect sensors. One or moremagnetic sensors 240 may detect changes in magnetic field or the likeand convert such detected signals into electrical signals, which may beprocessed further by apparatus 100 as described in further detail below.

Still referring to FIG. 2 , one or more additional sensors may include alocation detection device such as without limitation a globalpositioning system (GPS) receiver 244, defined as a receiver thatreceives signals from the GPS, Global Navigation Satellite System(GLONASS), BeiDou, Galileo, and/or Navigation with Indian Constellation(NavIC) satellite constellations and/or similar emitting systems, thatcan calculate its location based on the time and/or phase difference ofthe received signals. At least a GPS receiver 244 may include withoutlimitation, a receiving antenna, accompanying circuits, and processing.One or more GPS receivers may be configured to determine an orientationof apparatus 100 in relation to the Earth's true North, and/or otherlocations that are fixed within a coordinate system such as a universaland/or Earth-centric coordinate system, using differential GPS, phasedifferences, and/or other methods to exploit satellite constellationsand their positions. One or more GPS receivers may be configured toreceive and determine the local time based on the time informationreceived from the satellite signals. One or more GPS receivers 244 mayreceive position and timing signals, and the like and convert suchdetected signals into electrical signals, which may be processed furtherby apparatus 100 as described in further detail below. Locationdetection may alternatively or additionally be determined by proximityto electromagnetic transmitters having known locations, includingwithout limitation location determination using cell towertriangulation, determination based on proximity to wireless routers, orthe like.

In an embodiment, and with further reference to FIG. 2 , two or more ofoptical camera 108, infrared camera 112, light radar component 116,ultrasound device 120, 3D detector 204, Radar Component 208, WavelengthDetector 212, Polarized Camera 216, Multispectral Camera 220,Hyperspectral Camera 224, and/or other sensor data, for instance, andwithout limitation, including audio sensor 124, chemical sensor 128,motion sensor 132, ionizing radiation sensor 228, seismic sensor 232,mass or weight sensor 236, magnetic sensor 240, and/or globalpositioning system receiver 244 may function together as a fusioncamera. A “fusion camera,” as used in this disclosure, is an imagingdevice 104 that receives two or more different kinds of imaging or otherspatially derived data, which may be combined to form an image combiningthe two or more different kinds of imaging or other spatially deriveddata. For instance, and without limitation, light radar data may besuperimposed upon and/or combined with data captured using an opticaland/or infrared camera 112, enabling a coordinate system more accuratelyto capture depth in a resulting three-dimensional image. As a furthernon-limiting example, edge detection using ToF as described above may beenhanced and/or corrected using edge detection image processingalgorithms as described in further detail below. Infrared imaging may beused to verify that a subject 308 depicted in an image created by othermeans is likely a living human through body temperature detection.Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various alternative or additional ways inwhich different imaging data may be combined using a fusion camera totrack, identify, and/or understand detected elements.

Referring now to FIG. 3 , apparatus 100 may be mounted and/or deployedin a subject area. A “subject area,” as used in this disclosure, isregion within which apparatus 100 is configured to enforce one or moresecurity objectives. In an embodiment, subject area may include one ormore buildings, shopping centers, office spaces, zones, fields, and thelike thereof. In an embodiment, and without limitation, subject area mayinclude one or more residential homes and/or residential buildings.Security objectives may include exclusion of unauthorized persons fromsubject area, prevention of unauthorized persons from entering a door insubject area, prevention of unauthorized persons from accessing an itemto be protected 304, such as a valuable and/or dangerous item,protection of a person or object in subject area from harm, preventionof harm to apparatus 100, or the like. Subject area may include, withoutlimitation, a room, corridor, or other internal space, a fixed outdoorarea such as a porch, patio, gazebo, stage, or the like, a geometricallydefined area around a handheld or drone-mounted device such as acylindrical and/or spherical area defined by a given radius, or thelike. A user may use a computing device such as a control station,desktop computer, tablet, laptop, or the like to set bounds of subjectarea; alternatively or additionally, apparatus 100 may use automateddetection using any imaging device 104 or the like to image and/or scanthe subject area for processing to determine the locations of walls,objects, animals, features, or other boundary delineators to findpotential boundaries, which a user may confirm from a user computingdevice.

Still referring to FIG. 3 , apparatus 100 may use one or more imagingdevices 104 to determine a baseline condition of subject area. Imagingdevice 104 may map points within subject area to a coordinate system.Coordinate system may include x and y coordinates, which may correspondto axes on one or more focal planes of imaging device 104, substantiallyhorizontal and vertical axes, or the like, and a z coordinatecorresponding to depth and/or distance from imaging device 104. Depthmay be determined using image analysis such as parallax and/or analysisof relative sizes of objects, and/or using ToF range-finding, and/orartificial intelligence inferred methods, and/or shape from motion orpolarimetry, and/or computational vision methods and algorithms.Optical, infrared, light radar devices, RF sensors, radar components,THz imagers, MMW imagers, polarized cameras, multispectral cameras,and/or hyperspectral cameras may each be used to register boundaries ofsubject area and/or a geometric center thereof to coordinate system.Objects within subject area may then be located within coordinate systemto establish a baseline condition.

In an embodiment, and with continued reference to FIG. 3 , each separatecamera, motion detector, or other component and/or device in apparatus100 may have an associated Cartesian and/or polar coordinate system.Apparatus 100 may relate each such separate coordinate system to amaster coordinate system used by apparatus 100, using withoutlimitation, one or more affine transformations, rotationaltransformations, perspective transformations, perspectivetransformations, and/or scaling operations, sometimes collectivelyreferred to as a “homography transformation” or “homography.”

With further reference to FIG. 3 , one or more subjects 308 may bedetected via detection of changes to a baseline condition. A “subject,”as used herein, is a person, animal, object phenomenon, and/or substanceintroduced into subject area after baseline has been established. Asubject 308 may include one or more persons who are authorized to entersubject area and/or one or more persons who are not authorized to entersubject area. Subjects 308 may be identified, tracked, imaged, recorded,analyzed, hailed, marked and/or subjected to deterrent actions byapparatus 100 while within subject area, as described in further detailbelow. In an embodiment, and without limitation, subject 308 may beidentified as a function of one or more anonymous qualifications. Asused in this disclosure an “anonymous qualification” is a metric and/orelement denoting a unique parameter of an individual. For example, andwithout limitation, anonymous qualifications may include a gender,ethnicity, age, weight, facial, hair, eye color, and the like thereof.In a non-limiting embodiment, apparatus 100 may track anonymousqualifications of subject and/or provide such qualifications to personsor other devices without providing additional data usable to identifythe subject as a particular person; in an embodiment, this may be usedto preserve the privacy of a subject while still being able todistinguish the subject from other persons in an area and/or subjects.

Referring again to FIG. 1 , apparatus 100 may include one or moreadditional sensors. One or more additional sensors may include, withoutlimitation, at least an audio sensor 124. At least an audio sensor 124may include, without limitation, one or more microphones such as MEMSmicrophones. One or more microphones may be configured to detect soundsmade by subjects 308 in a subject area, such as people, animals, and/ormachines. One or more microphones may include one or more directionalmicrophones, defined as microphones that are exclusively orpreferentially sensitive to sound emanated from a particular directionwithin subject area; directional microphones may include directionalMEMS microphones, microphone arrays, acousto-optical detectors, and/oracoustic velocity sensors (AVS). One or more audio sensors 124 maydetect audio signals, direction of emanation of audio signals, and thelike and convert such detected signals and/or directional data intoelectrical signals, which may be processed further by apparatus 100 asdescribed in further detail below.

Still referring to FIG. 1 , one or more additional sensors may include achemical sensor 128, defined as a sensor that detects airborne chemicalssuch as gases, aerosols, biological and/or particulate matter. Achemical sensor 128 may include, without limitation a MEMS chemicalsensor 128, which may detect, without limitation, volatile organiccompounds (VOCs), carbon dioxide and/or a compound suitable forestimation of carbon dioxide such as hydrogen gas, flammable volatiles,gases associated with explosives and/or exhaust of firearms, or thelike. Chemical sensor 128 may be used to detect, without limitation,chemicals consistent with human presence, such as CO₂, H₂, or the like.Chemical sensor 128 may be used to detect one or more hazardouschemicals and/or chemicals associated with weaponry, such asnitroaromatic compounds for detection of explosives, gun powder and/orgun oils. Chemical sensor 128 may be used to detect one or morehazardous chemicals and/or chemicals associated with chemical and/orbiological warfare, such as nerve agents (such as sarin, soman,cyclohexylsarin, tabun, VX), blistering agents (such as mustards,lewisite), choking agents or lung toxicants (such as chlorine, phosgene,diphosgene), cyanides, incapacitating agents (such as anticholinergiccompounds), lacrimating agents (such as pepper gas, chloroacetophenone,CS), vomiting agents (such as adamsite), and/or biological agents (suchas anthrax, smallpox, plague, tularemia, and/or other detrimentalbacteria, viruses, prions). Persons skilled in the art, upon reviewingthe entirety of this disclosure, will be aware of various forms ofchemical sensors 128 that may be deployed, as well as various chemicalphenomena that chemical sensors 128 may be used to detect.

With continued reference to FIG. 1 , one or more additional sensors mayinclude a motion sensor 132 configured to detect motion in three or moredimensions and/or orientation in three dimensions of apparatus 100, forinstance when apparatus 100 is mounted to a drone and/or being used as ahandheld device. At least a motion sensor 132 may include, withoutlimitation, a MEMS sensor. At least a motion sensor 132 may include,without limitation, an inertial measurement unit (IMU). At least amotion sensor 132 may include one or more accelerometers; one or moreaccelerometers may include a plurality of accelerometers, such as threeor more accelerometers positioned to span three dimensions of possibleacceleration, so that any direction and magnitude of acceleration inthree dimensions may be detected and measured in three dimensions. Atleast a motion sensor 132 may include one or more gyroscopes; one ormore gyroscopes may include a plurality of gyroscopes, such as three ormore gyroscopes positioned to span three dimensions of possibleacceleration, so that any direction and magnitude of change in angularposition in three dimensions may be detected and measured in threedimensions. At least a motion sensor 132 may include, withoutlimitation, one or more magnetic sensors or magnetometers such as Halleffect sensors, compasses such as solid-state compasses, or the like;one or more magnetometers may include a plurality of magnetometers, suchas three or more magnetometers positioned to span three dimensions ofpossible orientation, so that any direction and magnitude of change inmagnetic field in three dimensions may be detected and measured in threedimensions, possibly for measurement of the apparatus' 100 orientationto the Earth's true North or detection of magnetic anomalies. Personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various components and/or devices that may be used as atleast a motion sensor 132 consistently with this disclosure.

Still referring to FIG. 1 , apparatus 100 includes a processor 136communicatively connected to the imaging device 104. Processor 136 mayinclude any computing device and/or processor 136 as described in thisdisclosure, including without limitation a microcontroller,microprocessor 136, digital signal processor 136 (DSP), graphicsprocessing unit (GPU), vision processing unit (VPU), field programmablegate array (FPGA), artificial intelligence accelerator, neural netaccelerator, and/or system on a chip (SoC) as described in thisdisclosure. As used herein, a device, component, or circuit is“communicatively connected” where the device, component, or circuit isable to receive data from and/or transmit data to another device,component, or circuit. In an embodiment, devices are placed incommunicative connection by electrically coupling at least an output ofone device, component, or circuit to at least an input of anotherdevice, component, or circuit. Devices may further be placed incommunicatively connection by creating an optical, inductive, or othercoupling between two or more devices. Communicatively connected devicemay be placed in near field communication with one another. Two or moredevices may be communicatively connected where the two or more devicesare configured to send and/or receive signals to or from each other.Placement of devices in communicative connection may include direct orindirect connection and/or transmission of data; for instance, two ormore devices may be connected or otherwise communicatively connected byway of an intermediate circuit. Placement of devices in communicativeconnection with each other may be performed via a bus or other facilityfor intercommunication between elements of a computing device 508 asdescribed in further detail below. Placement of devices in communicativeconnection with each other may include fabrication together on a sharedintegrated circuit and/or wafer; for instance, and without limitation,two or more communicatively coupled devices may be combined in a singlemonolithic unit or module.

Further referring to FIG. 1 , processor 136 may be communicativelyconnected to a remote device 140. Remote device 140 may include anycomputing device as described in this disclosure. Remote device 140 mayinclude, be included in, and/or communicate with a computer, laptop,and/or mobile device such as a mobile telephone or smartphone. Remotedevice 140 may include a single computing device operatingindependently, or may include two or more computing device operating inconcert, in parallel, sequentially or the like; two or more computingdevices may be included together in a single computing device or in twoor more computing devices. Remote device 140 may interface orcommunicate with one or more additional devices as described below infurther detail via a network interface device. Network interface devicemay be utilized for connecting remote device 140 to one or more of avariety of networks, and one or more devices. Examples of a networkinterface device include, but are not limited to, a network interfacecard (e.g., a mobile network interface card, an LTE card, a 5G card, afiber optic network card, a power over ethernet (PoE) card, a LAN card),a modem, and any combination thereof. Examples of a network include, butare not limited to, a wide area network (e.g., the Internet, anenterprise network), a metropolitan area network (e.g., a privatenetwork setup specifically for use amongst several buildings orphysically close locations), a cloud network (e.g., a network associatedwith hosted storage and/or processing off-site with parts locatedlocally or distributed), a local area network (e.g., a networkassociated with an office, a building, a campus or other relativelysmall geographic space), a telephone network, a data network associatedwith a telephone/voice provider (e.g., a mobile communications providerdata and/or voice network), a direct connection between two computingdevices, and any combinations thereof. A network may employ a wiredand/or a wireless mode of communication. In general, any networktopology may be used. Information (e.g., data, software etc.) may becommunicated to and/or from a computer and/or a computing device. Remotedevice 140 may include but is not limited to, for example, a computingdevice or cluster of computing devices in a first location and a secondcomputing device or cluster of computing devices in a second location.Remote device 140 may include one or more computing devices dedicated todata storage, security, distribution of traffic for load balancing, andthe like. Remote device 140 may distribute one or more computing tasksas described below across a plurality of computing devices of computingdevice, which may operate in parallel, in series, redundantly, or in anyother manner used for distribution of tasks or memory between computingdevices. Remote device 140 may be implemented using a “shared nothing”architecture in which data is cached at the worker, in an embodiment,this may enable scalability of system 100 and/or computing device.

Still referring to FIG. 1 , processor 136 and/or remote device 140 maybe designed and/or configured to perform any method, method step, orsequence of method steps in any embodiment described in this disclosure,in any order and with any degree of repetition. For instance, processor136 and/or remote device 140 may be configured to perform a single stepor sequence repeatedly until a desired or commanded outcome is achieved;repetition of a step or a sequence of steps may be performed iterativelyand/or recursively using outputs of previous repetitions as inputs tosubsequent repetitions, aggregating inputs and/or outputs of repetitionsto produce an aggregate result, reduction or decrement of one or morevariables such as global variables, and/or division of a largerprocessing task into a set of iteratively addressed smaller processingtasks. Processor 136 and/or remote device 140 may perform any step orsequence of steps as described in this disclosure in parallel, such assimultaneously and/or substantially simultaneously performing a step twoor more times using two or more parallel threads, processor 136 cores,processor 136 cores of other apparatus 100 or processors of remotedevices 140 in the network, or the like; division of tasks betweenparallel threads and/or processes may be performed according to anyprotocol suitable for division of tasks between iterations. Personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various ways in which steps, sequences of steps, processingtasks, and/or data may be subdivided, shared, or otherwise dealt withusing iteration, recursion, and/or parallel processing.

With continued reference to FIG. 1 , processor 136 is configured toidentify at least a subject 308 as a function of detection of subject308 using imaging device 104 and/or additional sensors on the apparatus100. As used in this disclosure, “identification” may includedifferentiation of subject from other persons, animals, and/or objectsin or near subject area, assignment of a district and/or uniqueidentifier to subject, identification of subject as a particular person,species, member of a group, or the like, or any other association ofdata with subject that enables tracking subject specifically withinsubject area, for instance and without limitation as set forth infurther detail below. Processor 136 may periodically and/or continuouslypoll imaging device 104 and/or other sensors to determine whether achange from baseline has occurred; for instance, apparatus 100 mayperiodically scan room using light radar, take photo/video data, or thelike. Processor 136 may iteratively compare baseline data to polledand/or event-driven data to detect changes.

In an embodiment, processor 136 and/or remote device 140 may perform oneor more image or signal processing algorithms to identify and/or trackobjects in subject area, which objects may include subjects 308 or otherobjects of interest. Initial identification of objects may be performedusing an edge detection algorithm. An “edge detection algorithm,” asused in this disclosure, includes a mathematical method that identifiespoints in a digital image at which the image brightness changes sharplyand/or has discontinuities. In an embodiment, such points may beorganized into straight and/or curved line segments, which may bereferred to as “edges.” Edge detection may be performed using anysuitable edge detection algorithm, including without limitation Cannyedge detection, Sobel operator edge detection, Prewitt operator edgedetection, Laplacian operator edge detection, and/or Differential edgedetection. Edge detection may include phase congruency-based edgedetection, which finds all locations of an image where all sinusoids inthe frequency domain, for instance as generated using a Fourierdecomposition, may have matching phases which may indicate a location ofan edge. Other methods for tracking, for instance, and withoutlimitation, may include algorithms, such as AdaBoost, BOOSTING, MultipleInstance Learning (MIL), Generic Object Tracking Using RegressionNetworks, Kernelized Correlation Filter, Medial Flow Tracker, MinimumOutput Sum of Squared Error, Tracking/Learning/Detection, Channel andSpatial Reliability, and/or other similar algorithms. Persons skilled inthe art, upon reviewing the entirety of this disclosure, will be awareof various ways in which the object can track of objects of interestusing various image and signal processing algorithms.

In an embodiment, processor 136 and/or remote device 140 may applyalgorithms to smooth or enhance tracking of moving objects in thesubject area to enhance accuracy of pointing devices and minimize error,hysteresis, and/or latency of deterrents. These include, but are notlimited to, Kalman filters, derivative controls,proportional/integral/derivative (PID) controls, moving averages,weighted averages, and/or other noise and error mitigation techniques.

Still referring to FIG. 1 , object detection and/or edge detection mayalternatively or additionally be performed using light radar data, RFsensor or radar component data, and/or 3D camera, sensor orcomputational method data. For instance, and without limitation,processor 136 and/or remote device 140 may receive, from light radarcomponent 116 and/or radar component 208, raw modulated images.Processor 136 and/or remote device 140 may use a graphical processingunit (GPU) or alternative acceleration processors as described forProcessor 136 to implement accelerated depth engine software convertingraw signal into depth maps; edges may be detected using depth map data.In a non-limiting embodiment, light radar pixels may be invalidated whenthey contain a saturated signal from overexposure. When pixels aresaturated, phase information may be lost. Pixels may alternatively oradditionally be invalidated if they received signals from more than oneobject in subject area. A common case where this sort of invalidationmay be seen is in corners. Because of detected geometry, light fromlight source may be reflected off one wall and onto another. Thisreflected light may cause ambiguity in measured depth of a pixel.Filters in depth algorithm may be used to detect these ambiguous signalsand invalidate such pixels. Another common case of multipath may becaused by pixels that contain a mixed signal from foreground andbackground, such as around object edges. Invalidation of pixels aroundedges may be accentuated due to fast motion of objects such as subject308. Thus, processor 136 and/or remote device 140 may detect an edge bydetection of a fringe of ambiguous and/or invalidated pixels; edge maybe confirmed by comparing ToF depths on either side of such a fringe,which may demonstrate depth differences consistent with a figure such asa subject 308 standing in front of a more distant background. ToF edgedetection and computer vision edge detection may be combined to identifyedges with a high degree of certainty.

Still referring to FIG. 1 , processor 136 and/or a remote device 140 maybe configured to perform one or more corrective processes to counteractdistortions such as parallax and/or pincushion distortions; this may beperformed, without limitation, by comparing geometries within subjectarea as captured by an optical camera 108 to geometries as detectedusing light radar and/or ToF devices as described above. For example andwithout limitation, a LIDAR image in two-dimensions may be orientedand/or overlaid such that a representative three-dimensional objectand/or shape is established, wherein an imaging device, sensor, and/orcircuit connected thereto converts the image to cartesian mapping suchthat a barrel distorted image is identified. The galvanometer may thenbe instructed to project a matrix and/or homographic matrix with aplurality of lines and vertices. A flat field corrected camera and/orsensor may then be instructed to determine the magnitude of distortionfrom the matrix, wherein a corrective homography matrix is determinedbased off several images taken of the calibration pattern that isprovided. This homography matrix can then be used by processor 136 orother parts of the apparatus 100 to calculate a corrected image, forinstance by multiplying the homography matrix to the image data.Multiple cameras can use this method to create alignment and scalinghomography matrices to correct off axis optical paths, distortionerrors, rotational errors, and other inconsistencies between multiplecameras and/or sensors. In an embodiment, and without limitation,calibration patters may include a flat card comprising a checkerboardpattern, wherein imaging device 104 and/or optical camera 108 detectsvertices and creates an alignment to the homography matrix. For example,and without limitation, processor 136 and/or remote device 140 maydetect one or more barrel distortions and/or rotations, wherein thecalibration corrects for the distortions as a function of a flat field.In some embodiments, calibration patterns and the sensor's derivedhomography matrices may be used in various methods and/or processes fordistortion correction. For instance, a first sensor may be chosen as aprimary sensor, and intrinsically calibrated to remove barrel distortionand pincushioning. Multiple additional sensors may each be firstintrinsically calibrated to have a flat field of vision. Subsequently,and all other sensors thus calibrated may be further calibrated againstthe primary sensor to determine their homography matrices in order toall be aligned and have a shared relative coordinate system, which mayrequire translational and/or affine matrices to adjust for rotations,displacements, and the like.

Further referring to FIG. 1 , homography matrices or other similarmethods may alternatively or additionally be used to calibratereflective or other beam steering devices such as galvanometers,fast-steering mirrors, and/or other beam steering devices used to aimdirected light or other emissive deterrents, camera and/or imaginginputs, ToF inputs or outputs, or the like. In an embodiment, a laser orother directed and/or focused light source may be output through such areflective device to form a calibration pattern on a surface such as awall or screen. The calibration pattern may include, without limitation,a 6×4 matrix or other geometrical shapes that provide the necessarycontrast. The calibration pattern, due to intrinsic errors in the beamsteering apparatus, may be output with a barrel distortion, pincushion,rotational, affine, perspective, or other geometrical errors; thedistorted image may be captured by a camera or other sensor that has itsintrinsic errors corrected using methods described above. The apparatusmay use the sensor's image to detect points or patterns of the captured,distorted calibration pattern as keypoints or centroids, then using theimage or data, generate a homography matrix to cause the centroids to becorrected to a flat matrix; this homography matrix may be used tocorrect reflective device, causing output to be flat in the same manneras a flat camera image without intrinsic errors. A flat output field ofreflective device may then be registered to a field of vision of primarysensor to ensure that output of directed light deterrent is accuratelyaimed within a coordinate system of primary and/or other sensors.

Still referring to FIG. 1 , if the optics of a beam steering device arenot aligned perfectly, there may be drift based on the optics' positionsrelative to each other; for instance, for a variable beam expander, asthe apparatus sweeps through magnifications or other optical changes,misalignment may cause the beam or optical path to divert by a fewmicroradian (prad) every step the assembly moves or changes. This maycause significant error at far distances. In order to overcome this, theapparatus may be configured to calibrate this drift by mapping aposition of the laser beam with all or critical perturbations of theopto-mechanics and/or other parts that would affect the directionalityof beam or optical path and record a deviation from a base configurationusing a camera or other sensors. An image may be processed such that asingle pic is taken for each position of the components by finding thecentroid of each beam spot, such as using a threshold of the image, thencontour and/or center finding of the results. This may then be storedwith regard to current settings of optics. The above-described processmay be repeated for every optical component and/or component that mayaffect a laser that can be manipulated for a minimum and/or maximum andstepped in between to a degree and accuracy needed to achieve errorrequirements for aiming the laser. Apparatus may then create acorrection method such as a lookup table, and may interpolatecorrections, for instance using a quadratic fit function, or the like,between values therefrom. Apparatus may then apply corrections to beamsteering or other necessary optical, mechanical, or other affectedcomponents.

Still referring to FIG. 1 , if other components, such as acoustic orkinetic countermeasures, are used, cameras and sensors may be used toalign them. For example, to align a beam-forming acoustic array,microphones may be placed in the far field at several locations in asensor's field of view. Microphones may have an optical marker, orfiducial, on them so each microphone can be uniquely identified byapparatus and/or system. Apparatus may then sweep a sound source inspace, with each microphone noting when maximum sound pressure isreached at given steering values, along with the microphones' locationsin the sensor's coordinate system. These values may then be used todetermine alignment of acoustical sound patterns to other sensors andsystems on apparatus.

Referring now to FIG. 4 , processor 136 and/or remote device 140 may beconfigured to perform one or more machine-learning processes to analyzedata captured by sensors, feedback loops, and/or imaging devices 104.Such processes may be performed using a machine-learning module 400,which may include any processor 136, computing device, and/or hardwareand/or software component thereof, as described in this disclosure.Machine-learning module may perform determinations, classification,and/or analysis steps, methods, processes, or the like as described inthis disclosure using machine learning processes. A “machine learningprocess,” as used in this disclosure, is a process that automatedly usestraining data 404 to generate an algorithm that will be performed by acomputing device/module to produce outputs 408 given data provided asinputs 412; this is in contrast to a non-machine learning softwareprogram where the commands to be executed are determined in advance by auser and written in a programming language.

Still referring to FIG. 4 , “training data,” as used herein, is datacontaining correlations that a machine-learning process may use to modelrelationships between two or more categories of data elements. Forinstance, and without limitation, training data 404 may include aplurality of data entries, each entry representing a set of dataelements that were recorded, received, and/or generated together; dataelements may be correlated by shared existence in a given data entry, byproximity in a given data entry, or the like. Multiple data entries intraining data 404 may evince one or more trends in correlations betweencategories of data elements; for instance, and without limitation, ahigher value of a first data element belonging to a first category ofdata element may tend to correlate to a higher value of a second dataelement belonging to a second category of data element, indicating apossible proportional or other mathematical relationship linking valuesbelonging to the two categories. Multiple categories of data elementsmay be related in training data 404 according to various correlations;correlations may indicate causative and/or predictive links betweencategories of data elements, which may be modeled as relationships suchas mathematical relationships by machine-learning processes as describedin further detail below. Training data 404 may be formatted and/ororganized by categories of data elements, for instance by associatingdata elements with one or more descriptors corresponding to categoriesof data elements. As a non-limiting example, training data 404 mayinclude data entered in standardized forms by persons or processes, suchthat entry of a given data element in a given field in a form may bemapped to one or more descriptors of categories. Elements in trainingdata 404 may be linked to descriptors of categories by tags, tokens, orother data elements; for instance, and without limitation, training data404 may be provided in fixed-length formats, formats linking positionsof data to categories such as comma-separated value (CSV) formats and/orself-describing formats such as extensible markup language (XML),JavaScript Object Notation (JSON), or the like, enabling processes ordevices to detect categories of data.

Alternatively or additionally, and continuing to refer to FIG. 4 ,training data 404 may include one or more elements that are notcategorized; that is, training data 404 may not be formatted or containdescriptors for some elements of data. Machine-learning algorithmsand/or other processes may sort training data 404 according to one ormore categorizations using, for instance, natural language processingalgorithms, tokenization, detection of correlated values in raw data andthe like; categories may be generated using correlation and/or otherprocessing algorithms. As a non-limiting example, in a corpus of text,phrases making up a number “n” of compound words, such as nouns modifiedby other nouns, may be identified according to a statisticallysignificant prevalence of n-grams containing such words in a particularorder; such an n-gram may be categorized as an element of language suchas a “word” to be tracked similarly to single words, generating a newcategory as a result of statistical analysis. Similarly, in a data entryincluding some textual data, a person's name may be identified byreference to a list, dictionary, or other compendium of terms,permitting ad-hoc categorization by machine-learning algorithms, and/orautomated association of data in the data entry with descriptors or intoa given format. The ability to categorize data entries automatedly mayenable the same training data 404 to be made applicable for two or moredistinct machine-learning algorithms as described in further detailbelow. Training data 404 used by machine-learning module 400 maycorrelate any input data as described in this disclosure to any outputdata as described in this disclosure.

Further referring to FIG. 4 , images and/or geometry detected in images,such as without limitation as detected using edge detection, ToFprocesses, or the like, may be filtered, sorted, and/or selected usingone or more supervised and/or unsupervised machine-learning processesand/or models as described in further detail below; such models mayinclude without limitation a classifier 416. Classifier 416 may includea “classifier,” which as used in this disclosure is a machine-learningmodel as defined below, such as a mathematical model, neural net, orprogram generated by a machine learning algorithm known as a“classification algorithm,” as described in further detail below, thatsorts inputs into categories or bins of data, outputting the categoriesor bins of data and/or labels associated therewith. A classifier may beconfigured to output at least a datum that labels or otherwiseidentifies a set of data that are clustered together, found to be closeunder a distance metric as described below, or the like.Machine-learning module 400 may generate a classifier using aclassification algorithm, defined as a process whereby a computingdevice and/or any module and/or component operating thereon derives aclassifier from training data 404. Classification may be performedusing, without limitation, linear classifiers such as without limitationlogistic regression and/or naive Bayes classifiers, nearest neighborclassifiers such as k-nearest neighbors classifiers, support vectormachines, least squares support vector machines, fisher's lineardiscriminant, quadratic classifiers, decision trees, boosted trees,random forest classifiers, learning vector quantization, and/or neuralnetwork-based classifiers.

As a non-limiting example, a classifier a used in this disclosure may begenerated, using a Naïve Bayes classification algorithm. Naïve Bayesclassification algorithm generates classifiers by assigning class labelsto problem instances, represented as vectors of element values. Classlabels are drawn from a finite set. Naïve Bayes classification algorithmmay include generating a family of algorithms that assume that the valueof an element is independent of the value of any other element, given aclass variable. Naïve Bayes classification algorithm may be based onBayes Theorem expressed as P(A/B)=P(B/A) P(A)÷P(B), where P(A/B) is theprobability of hypothesis A given data B also known as posteriorprobability; P(B/A) is the probability of data B given that thehypothesis A was true; P(A) is the probability of hypothesis A beingtrue regardless of data also known as prior probability of A; and P(B)is the probability of the data regardless of the hypothesis. A naiveBayes algorithm may be generated by first transforming training datainto a frequency table. Portable computing device 104 may then calculatea likelihood table by calculating probabilities of different dataentries and classification labels. Processor 136 and/or remote device140 may utilize a naive Bayes equation to calculate a posteriorprobability for each class. A class containing the highest posteriorprobability is the outcome of prediction. Naïve Bayes classificationalgorithm may include a gaussian model that follows a normaldistribution. Naïve Bayes classification algorithm may include amultinomial model that is used for discrete counts. Naïve Bayesclassification algorithm may include a Bernoulli model that may beutilized when vectors are binary.

Still referring to FIG. 4 , machine-learning module 400 may beconfigured to perform a lazy-learning process 420 and/or protocol, whichmay alternatively be referred to as a “lazy loading” or“call-when-needed” process and/or protocol, may be a process wherebymachine learning is conducted upon receipt of an input to be convertedto an output, by combining the input and training set to derive thealgorithm to be used to produce the output on demand. For instance, aninitial set of simulations may be performed to cover an initialheuristic and/or “first guess” at an output and/or relationship. As anon-limiting example, an initial heuristic may include a ranking ofassociations between inputs and elements of training data 404. Heuristicmay include selecting some number of highest-ranking associations and/ortraining data 404 elements. Lazy learning may implement any suitablelazy learning algorithm, including without limitation a K-nearestneighbors algorithm, a lazy naive Bayes algorithm, or the like; personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various lazy-learning algorithms that may be applied togenerate outputs as described in this disclosure, including withoutlimitation lazy learning applications of machine-learning algorithms asdescribed in further detail below.

Alternatively or additionally, and with continued reference to FIG. 4 ,machine-learning processes as described in this disclosure may be usedto generate machine-learning models 424. A “machine-learning model,” asused in this disclosure, is a mathematical and/or algorithmicrepresentation of a relationship between inputs and outputs, asgenerated using any machine-learning process including withoutlimitation any process as described above, and stored in memory; aninput is submitted to a machine-learning model 424 once created, whichgenerates an output based on the relationship that was derived. Forinstance, and without limitation, a linear regression model, generatedusing a linear regression algorithm, may compute a linear combination ofinput data using coefficients derived during machine-learning processesto calculate an output datum. As a further non-limiting example, amachine-learning model 424 may be generated by creating an artificialneural network, such as a convolutional neural network comprising aninput layer of nodes, one or more intermediate layers, and an outputlayer of nodes. Connections between nodes may be created via the processof “training” the network, in which elements from a training data 404set are applied to the input nodes, a suitable training algorithm (suchas Levenberg-Marquardt, conjugate gradient, simulated annealing, orother algorithms) is then used to adjust the connections and weightsbetween nodes in adjacent layers of the neural network to produce thedesired values at the output nodes. This process is sometimes referredto as deep learning.

Still referring to FIG. 4 , machine-learning algorithms may include atleast a supervised machine-learning process 428. At least a supervisedmachine-learning process 428, as defined herein, include algorithms thatreceive a training set relating a number of inputs to a number ofoutputs, and seek to find one or more mathematical relations relatinginputs to outputs, where each of the one or more mathematical relationsis optimal according to some criterion specified to the algorithm usingsome scoring function. For instance, a supervised learning algorithm mayinclude inputs as described in this disclosure as inputs, outputs asdescribed in this disclosure as outputs, and a scoring functionrepresenting a desired form of relationship to be detected betweeninputs and outputs; scoring function may, for instance, seek to maximizethe probability that a given input and/or combination of elements inputsis associated with a given output to minimize the probability that agiven input is not associated with a given output. Scoring function maybe expressed as a risk function representing an “expected loss” of analgorithm relating inputs to outputs, where loss is computed as an errorfunction representing a degree to which a prediction generated by therelation is incorrect when compared to a given input-output pairprovided in training data 404. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of variouspossible variations of at least a supervised machine-learning process428 that may be used to determine relation between inputs and outputs.Supervised machine-learning processes may include classificationalgorithms as defined above.

Further referring to FIG. 4 , machine learning processes may include atleast an unsupervised machine-learning processes 432. An unsupervisedmachine-learning process, as used herein, is a process that derivesinferences in datasets without regard to labels; as a result, anunsupervised machine-learning process may be free to discover anystructure, relationship, and/or correlation provided in the data.Unsupervised processes may not require a response variable; unsupervisedprocesses may be used to find interesting patterns and/or inferencesbetween variables, to determine a degree of correlation between two ormore variables, or the like.

Still referring to FIG. 4 , machine-learning module 400 may be designedand configured to create a machine-learning model 424 using techniquesfor development of linear regression models. Linear regression modelsmay include ordinary least squares regression, which aims to minimizethe square of the difference between predicted outcomes and actualoutcomes according to an appropriate norm for measuring such adifference (e.g. a vector-space distance norm); coefficients of theresulting linear equation may be modified to improve minimization.Linear regression models may include ridge regression methods, where thefunction to be minimized includes the least-squares function plus termmultiplying the square of each coefficient by a scalar amount topenalize large coefficients. Linear regression models may include leastabsolute shrinkage and selection operator (LASSO) models, in which ridgeregression is combined with multiplying the least-squares term by afactor of 1 divided by double the number of samples. Linear regressionmodels may include a multi-task lasso model wherein the norm applied inthe least-squares term of the lasso model is the Frobenius normamounting to the square root of the sum of squares of all terms. Linearregression models may include the elastic net model, a multi-taskelastic net model, a least angle regression model, a LARS lasso model,an orthogonal matching pursuit model, a Bayesian regression model, alogistic regression model, a stochastic gradient descent model, aperceptron model, a passive aggressive algorithm, a robustnessregression model, a Huber regression model, or any other suitable modelthat may occur to persons skilled in the art upon reviewing the entiretyof this disclosure. Linear regression models may be generalized in anembodiment to polynomial regression models, whereby a polynomialequation (e.g. a quadratic, cubic or higher-order equation) providing abest predicted output/actual output fit is sought; similar methods tothose described above may be applied to minimize error functions, aswill be apparent to persons skilled in the art upon reviewing theentirety of this disclosure.

Continuing to refer to FIG. 4 , machine-learning algorithms may include,without limitation, linear discriminant analysis. Machine-learningalgorithm may include quadratic discriminate analysis. Machine-learningalgorithms may include kernel ridge regression. Machine-learningalgorithms may include support vector machines, including withoutlimitation support vector classification-based regression processes.Machine-learning algorithms may include stochastic gradient descentalgorithms, including classification and regression algorithms based onstochastic gradient descent. Machine-learning algorithms may includenearest neighbors algorithms. Machine-learning algorithms may includeGaussian processes such as Gaussian Process Regression. Machine-learningalgorithms may include cross-decomposition algorithms, including partialleast squares and/or canonical correlation analysis. Machine-learningalgorithms may include naive Bayes methods. Machine-learning algorithmsmay include algorithms based on decision trees, such as decision tree148 classification or regression algorithms. Machine-learning algorithmsmay include ensemble methods such as bagging meta-estimator, forest ofrandomized tress, AdaBoost, gradient tree boosting, and/or votingclassifier methods. Machine-learning algorithms may include neural netalgorithms, including convolutional neural net processes.

Still referring to FIG. 4 , models may be generated using alternative oradditional artificial intelligence methods, including without limitationby creating an artificial neural network, such as a convolutional neuralnetwork comprising an input layer of nodes, one or more intermediatelayers, and an output layer of nodes. Connections between nodes may becreated via the process of “training” the network, in which elementsfrom a training data 404 set are applied to the input nodes, a suitabletraining algorithm (such as Levenberg-Marquardt, conjugate gradient,simulated annealing, or other algorithms) is then used to adjust theconnections and weights between nodes in adjacent layers of the neuralnetwork to produce the desired values at the output nodes. This processis sometimes referred to as deep learning. This network may be trainedusing training data 404.

With continued reference to FIG. 4 , Classifier may include an objectclassifier 436. Object classifier 436, which may be implemented usingany classification method and/or algorithm as described above, may beused to sort objects detected in subject area into categories and/ortypes of objects. For instance, and without limitation, objectclassifier may identify a first object detected using computer visiontechniques and/or ToF as described above as a human body, which may befurther classified as an adult and/or child, a second object as ananimal such as a dog, rat, racoon, bird, or the like, a third object asan inanimate object, which may be further classified as described below,or the like. Object classifier may classify and/or identify severalobjects that are in subject area simultaneously. For instance, andwithout limitation, object classifier may identify multiple persons aspersons, one or more objects being held by persons as belonging to oneor more categories of object, or the like.

Still referring to FIG. 4 , Classifier may include a tool classifier440. Tool classifier 440 may identify one or more objects held by or ona person of subject 308 by classification to one or more categories ofobject, such as tools, weapons, communication devices, or the like. Oneor more categories may identify such an object as, and/or distinguishobject from, a weapon, a tool usable for a break-in, a tool designed todamage objects and/or people, a tool capable of damaging objects and/orpeople, and/or an innocuous object such as a sandwich or coffee cup.

With further reference to FIG. 4 , once objects, persons, and/or othersubjects 308 are defined, imaging device 104 and/or processor 136 may beconfigured to track motion and/or actions of such persons and/or objectsrelative to apparatus 100. For instance, and without limitation, a labelmay be associated with each identified object and/or person, which maybe tracked subsequently. Similarly, labels may be associated withindividual anatomical elements and/or targets, which may also be trackedusing imaging device 104. A number of people in subject area may becalculated and/or tracked.

Still referring to FIG. 4 , Classifier may include an anatomicalclassifier 444. Anatomical classifier may identify, on a person (i.e.,an image element that has been classified as being a person), one ormore elements of anatomy. One or more elements of anatomy may include aface; in other words, anatomical classifier may identify a face of auser. Anatomical classifier may identify, and permit processor 136and/or remote device 140 to track, one or more anatomical landmarks suchas joints, eyes, or the like. For instance, and as illustrated forexemplary purposes in FIG. 7 , landmarks such as joints may beidentified and/or tracked according to position and/or orientation onone or more images 700 of a person. Landmark position and/or orientationmay be determined as estimates relative to a global depth sensor frameof reference and/or a frame of reference established using a coordinatesystem used with imaging device 104 as described above. Position may bespecified, without limitation, using distance computation within athree-dimensional coordinate system which may be computed in units oflength at a given resolution such as millimeters. Orientation may beexpressed using any suitable mathematical and/or geometrical constructsuch as vectors, quaternions, or the like, any of which may benormalized. A position and/or orientation of each landmark and/or jointmay form its own landmark and/or joint coordinate system. All landmarkand/or joint coordinate systems may be formed as absolute coordinatesystems relative to a 3D coordinate system used by and/or with imagingdevice 104. Landmark coordinates may be used in axis orientation.

In an embodiment, and now with continued reference to FIG. 5 ,identified landmarks may be organized and/or tracked according to ajoint hierarchy. For instance, and without limitation, a skeleton mayinclude 32 joints with a joint hierarchy flowing from a center of thebody to a plurality of extremities. Each connection, such as a bone, maylink a parent joint with a child joint. Processor 136 and/or remotedevice 140 may track relative positions of landmarks, in combinationwith joint hierarchy, to determine poses and/or actions of a personbeing tracked; poses and/or sequences of poses may be further classifiedto behaviors as described below. For instance, and without limitation,processor 136 and/or remote device 140 may track a position and/ororientation of a person's face and/or anatomical features thereon. Thismay be effected using a geometric object such as without limitation aface rectangle. A face rectangle, one or which may be associated witheach detected face, may mark a location and size of the face in animage. Rotation of face rectangle with respect to an image coordinatesystem may provide a simple and thus rapidly computable way to trackorientation and position of elements of a person's face, includingwithout limitation the eyes. A head pose attribute may alternatively oradditionally be generated and/or tracked to determine and/or render aposition of a person's facial or other anatomy.

Still referring to FIG. 5 , landmarks may include, as a non-limitingexample, a spinal landmark at the sternum 502, a spinal landmark at thenaval region 504, a pelvic landmark 506, a right hip landmark 508, aleft hip landmark 510, a right knee landmark 512, a left knee landmark514, a right ankle landmark 516, a left ankle landmark 518, a right footlandmark 520, a left foot landmark 522, a right clavicle landmark 524, aleft clavicle landmark 526, a right shoulder landmark 528, a leftshoulder landmark 530, a right elbow landmark 532, a left elbow landmark534, a right wrist landmark 536, a left wrist landmark 538, a right handlandmark 540, a left hand landmark 542, a right hand tip landmark 544, aleft hand tip landmark, 546, a right thumb landmark 548, a left thumblandmark 550, a neck landmark 552, a head landmark 554, a right earlandmark 556, a left ear landmark 558, a nose landmark 560, a right eyelandmark 562, and/or a left eye landmark 564.

In an embodiment, and continuing to refer to FIG. 5 , apparatus 100and/or anatomical classifier 444 may be designed to be agnostic to oneor more variations in appearance. One or more variations in appearancemay include without limitation variations in gender, ethnicity, skintone, height, weight, body composition, age, national origin,traditional clothing such as hijabs and/or yarmulkes, sartorial choices,and/or body modifications such as tattoos, piercings, or the like. In anembodiment, machine-learning models in apparatus such as withoutlimitation classifiers, may be trained using training data having imagesthat vary according to one or more such variations; for instance,training data may include training examples for each of various ethnicgroups, age brackets, body compositions, body modifications, traditionalclothing, or the like, and may also include training examples of any orall such variations for each sex and/or gender. In some embodiments,apparatus 100, anatomical classifier 444, and/or other classifiersand/or machine-learning models may be tested against a plurality ofpersons, where the plurality may be designed to span a possible range ofvariations as described above; identification of anatomical features,facial recognition, and/or other machine-learning and/or classificationoutputs may be tested for accuracy, for instance by a person and/orgroup of people acting as an auditor and/or test administrator. Whereaccuracy falls below a threshold level, additional training examplespertaining to variations for which accuracy is low may be used tofurther train models, classifiers, and/or apparatus 100. In anembodiment, approaches described above may function to prevent apparatus100 from behaving differently for people having different demographiccharacteristics, aiding in ensuring unbiased performance.

Still referring to FIG. 5 , sensitivity to human bias has risen tounprecedented levels across the globe. This is an all-consumingresponsibly for every company, law enforcement agency in the world. Manytechnology companies have created executive teams just to counteractinherent bias into AI algorithmic development. A deep resourcecommitment is beginning to be seen focused on this area with an emphasisof ethics and balance at its foundational cornerstone. Embodimentsdescribed herein may completely remove any nefarious profiling by onlyconsidering physical boundary conditions of a protected area along withviolators' actions to be markers for interdiction. Apparatus may beagnostic relating to a subject's ethnicity, gender or dress. Apparatusmay act in such a way as to produce no bias.

Referring again to FIG. 4 , anatomical classifier 444, tool classifier440, computer vision, and/or ToF may be combined to identify one or moreelements that are worn on or held by a person. For instance, and withoutlimitation, processor 136 and/or remote device 140 may perform glassesdetection. Glasses may be identified by detecting edges on one or moreregions of facial anatomy such as a nose-bridge area, an area around eyesockets, or the like; detection such edges may be used to identifyglasses, and/or geometry of such edges may be classified using a toolclassifier 440 or the like to determine that a user is wearing glasses.A user determined to be wearing glasses may be subjected to a differentthreat response as described in further detail below. Eyewear may, insome embodiments, be further classified to identify categories ofeyewear such as sunglasses, eyewear that protects against light,physical hazards, or the like, night-vision and/or infrared goggles,and/or visual corrective lenses, each of which may indicate a modifiedthreat response as described in further detail below.

Now back to FIG. 4 , machine-learning module 400 may include additionalelements that use identification of anatomical features, objects, and/orother visual data to determine and/or estimate further informationconcerning phenomena detected using sensor and/or imaging device 104.For instance, and without limitation, machine-learning module 400 mayinclude and/or generate a behavior classifier 448. Behavior classifier448 may link one or more postures, actions, objects, and/or identity ofand/or associated with a person to one or more behaviors. For instance,a series of postures and/or movements indicating approach toward an itemto protect may be classified to an intent to abrogate protection of theitem. A series of postures separated by small increments of time mayindicate rapid motion, which may be classified based on reduction ofdistance to apparatus 100 and/or item to protect, which may indicateaggressive intent, and/or increase of such distance, which may indicatean attempt to flee or otherwise vacate subject area. As a furtherexample, postures and/or movements may be classified, withoutlimitation, to aggressive acts such as throwing objects, smashingobjects, striking another person, forcing entry to one or morelocations, stomping on objects, vandalizing and/or defacing objectsand/or surfaces or the like. Behavior classifier 448 may alternativelyor additionally associate objects held by a person with potentialactions and/or behaviors, potentially in combination with poses and/oractions. For instance, and without limitation, an object identified as agun, knife, bludgeon, and/or other weapon may be associated withaggressive behaviors, while other objects such as containers, tools, orthe like may be associated with aggressive behavior only when associatedwith particular postures and/or actions; as a non-limiting example,actions associated with setting a fire, when combined with a box-likeobject may indicate likely use of contents of the box-like object as anincendiary, while an action associated with striking, throwing, and/orprying, combined with an identified tool usable to cause damage, steal,force access to an item, or the like may be classified to an aggressivebehavior.

Further referring to FIG. 4 , behavior classifier 448 may classifyobjects, persons, postures, and/or actions to aggression levels and/ordegrees of severity of behavior, which may be used in threat-levelcalculation as described in further detail below. Alternatively oradditionally, objects, persons, postures, and/or actions may be linkedto identifiers of specific behaviors; such behaviors may be linked tospecific responses as described in further detail below, and/or may belinked to degrees of severity and/or aggression levels. Behavioranalysis and/or classification may alternatively or additionally beperformed on verbal inputs captured by audio input devices, which may beconverted to textual form using speech-to-text software or the like andcompared to keywords or other linguistic elements. Keywords may includeone or more words associated with undesirable and/or dangerous actions,as determined by classifiers trained to associate particular words withsubsequent and/or concurrent actions, and/or using a look-up table ordatabase of keywords. The system may further process the speech forcharacteristics, such as, but not limited to, inflections, tones,variability, pace, and other features that may be correlated to moods,aggression levels, deceptive intentions, irritability, anger,frustration, and/or other emotions, feelings, and/or intents. The systemmay further track facial or other features, such as, but not limited to,eye movements, facial muscle movements, facial pore dilations, and/orother features to analyze a persons' intent, moods, aggression, possibleinfluence by chemicals, and/or other key behaviors.

Still referring to FIG. 4 , machine-learning module 400 may include aface recognition classifier 452. Face recognition classifier 452 may beconfigured to classify one or more images of a face of a subject 308 toa personal identity. Such classification may be done with a series ofimages of a subject 308's face; in an embodiment, classification of twoor more images of the face of the same subject 308 may be compared toone another, with an identification having greatest proximity, given adistance and/or proximity measurement used in a classifier as describedabove, across the different classifications may be treated as thecorrect identification. A degree of proximity of one or more images soclassified may be mapped to a likelihood of correct identification,which may be expressed as a probability; where likelihood is less than apreconfigured threshold amount, processor 136 and/or remote device 140may not treat subject 308 as identified. Repeated and/or iterativeclassification of facial images may be continued until an identificationis made. Threshold may be set at a default value and/or configuredand/or selected by a user of system.

With continued reference to FIG. 4 , facial recognition processes mayinclude identification of the visual landmarks of human faces and/or ofbounding-box locations. A face's features may be extracted and storedfor use in identification. Bounding box, visual landmarks, and/orfeatures may be stored as a representation of one face and used asinputs facial recognition classifier 452. Facial recognition classifier452 may be used to match a facial representation to a data structurerepresenting a person, including without limitation an identifier of aperson, a data structure including various attributes of a person, orthe like. Identification using facial recognition classifier may be usedto retrieve data concerning an identified subject 308 from one or moredatabases and/or datastores as described in further detail below. In anembodiment, a classifier may be trained to recognize a person at adistance; for instance, recent data taken at closer range may be used toidentify gross identifying markers, a plurality of which combined mayexceed a threshold probability that a person is subject. This may inturn be used to determine that a person at a greater distance is asubject to be deterred, permitting interdiction at distances of 100yards or more, so that a person can be headed off before attempting toenter a building, start shooting, or otherwise engage in nefariousactivities.

Further referring to FIG. 4 , classifiers may further perform voicerecognition. For instance, a voice recognition classifier in anembodiment may be trained using training data that correlates audiorecordings of a subject's voice with identification of the subject; suchidentification may be performed automatically using any other classifierdescribed above and/or may be entered by a user.

Still referring to FIG. 4 , machine-learning module 400 may include adeterrent effects classifier 456. Deterrent effects classifier 456 maybe configured to classify one or more images or data from sensors todetermine success or failure of a deterrent used on a subject 308. Thiscan be in the form of behaviors, movements, and/or other characteristicsthat provide information to the processor 136 to determine if adeterrent's application was successful or not. For example, if anoptical deterrent is used, the deterrent effects classifier can try todetermine if the person is still focused on an object in the subjectarea, or if they are dazzled and currently unable to progress.

Still referring to FIG. 4 , processor 136 and/or remote device 140 mayuse any or all sensor feedback and/or machine-learning to performliveliness detection, defined as a process used to distinguish a personfrom a static image or inanimate object, for instance by trackingmovements, classifying behavior, sensing body temperature, or the like.Processor 136 and/or remote device 140 may be configured todifferentiate between children and adults, through classification ofsize, developmental feature models, or the like. Processor 136 and/orremote device 140 may be configured to distinguish people from otheranimals such as dogs.

Still referring to FIG. 4 , a machine-learning model may include aprediction engine 460, which may be used, without limitation, to predicta probable direction of travel and/or other motion by subject based onone or more previous actions. Prediction engine 460 may include, withoutlimitation, a supervised machine-learning model, which may be generatedusing any supervised machine-learning process 428 as described above.Training data 404 used to generate prediction engine 460 may includeentries having sequences of motion vectors, whereby outputs of predictedmotion vectors given a previous sequence of motion vectors may betrained. Training data may alternatively or additionally includesequences velocity vectors and/or acceleration vectors, which may enableprediction engine to predict both a direction and rate of movement of asubject and/or a body part of subject given a sequence of one or moreprevious movements as described using one or more previous velocityvectors. Velocity vectors and/or other motion vectors may be representedas n-tuples, such as triples of numbers representing components ofmotion in three dimension according to a coordinate system and/or anysuitable set of three vectors spanning three dimensions, sextuplesdescribing both translational and rotational velocity vectors, or thelike. Prediction engine may, for instance generate a result that aperson who has moved his head to quickly to the right is most likely tomove his head sharply downward next, pivot it to the left, and/or anyother such description.

Referring now to FIG. 6 , an exemplary embodiment of neural network 600is illustrated. A neural network 600 also known as an artificial neuralnetwork, is a network of “nodes,” or data structures having one or moreinputs, one or more outputs, and a function determining outputs based oninputs. Such nodes may be organized in a network, such as withoutlimitation a convolutional neural network, including an input layer ofnodes, one or more intermediate layers, and an output layer of nodes.Connections between nodes may be created via the process of “training”the network, in which elements from a training dataset are applied tothe input nodes, a suitable training algorithm (such asLevenberg-Marquardt, conjugate gradient, simulated annealing, or otheralgorithms) is then used to adjust the connections and weights betweennodes in adjacent layers of the neural network to produce the desiredvalues at the output nodes. This process is sometimes referred to asdeep learning. Connections may run solely from input nodes toward outputnodes in a “feed-forward” network, or may feed outputs of one layer backto inputs of the same or a different layer in a “recurrent network.”

Referring now to FIG. 7 , an exemplary embodiment of a node of a neuralnetwork is illustrated. A node may include, without limitation aplurality of inputs x_(i) that may receive numerical values from inputsto a neural network containing the node and/or from other nodes. Nodemay perform a weighted sum of inputs using weights w_(i) that aremultiplied by respective inputs x_(i). Additionally or alternatively, abias b may be added to the weighted sum of the inputs such that anoffset is added to each unit in the neural network layer that isindependent of the input to the layer. The weighted sum may then beinput into a function φ, which may generate one or more outputs y.Weight w_(i) applied to an input x_(i) may indicate whether the input is“excitatory,” indicating that it has strong influence on the one or moreoutputs y, for instance by the corresponding weight having a largenumerical value, and/or a “inhibitory,” indicating it has a weak effectinfluence on the one more inputs y, for instance by the correspondingweight having a small numerical value. The values of weights w; may bedetermined by training a neural network using training data, which maybe performed using any suitable process as described above.

Referring again to FIG. 1 , processor 136 is configured to determine abehavior descriptor associated with the subject 308. A “behaviordescriptor,” as used in this disclosure, is an element of datadescribing a degree of threat and/or probability of taking an action bya subject. A behavior descriptor may describe a violation of a boundarycondition, where a “boundary condition” is an element of data describingone or more specific actions apparatus is configured to and/or deter asubject from performing. A boundary condition may include entrance into,egress from, and/or persistent presence in subject area and/or a subsetthereof. A boundary condition may include an action that exceeds athreshold level of speed, amount of time idle at or within a boundary,density of traffic at or within the boundary, or other measurablephysical parameter. A boundary condition may include performance of aspecific behavior and/or a range of behaviors apparatus is configured toprevent and/or deter. Different boundary conditions may be associatedwith different threat levels and/or deterrent modes. A behaviordescriptor may include a threat level represented by subject. A behaviordescriptor may include a current threat level, boundary conditionviolation, behavior, and/or identity of subject, and/or a future threatlevel, boundary condition violation, and/or behavior of subject. Personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various alternative or additional examples of behaviordescriptors and/or boundary conditions. Determination of a behaviordescriptor may include, without limitation, receiving a manual threatdetermination, which may be entered as a user command; for instance, auser may press a button, pulling a trigger, or otherwise enter a commandindicating that subject 308 is, or is not, a threat. In an embodiment,information concerning subject 308 may be displayed to user; suchinformation may include without limitation an identification of subject308 generated using facial recognition as described above, anautomatically generated threat-level determination as described infurther detail below, one or more images and/or video feeds of subject308 as captured using imaging device 104, or the like. Manual threatdetermination may include responding to an automatically determinedbehavior descriptor in the affirmative, or disagreement therewith.Threat determination may be hardwired or immediately tied to a threatresponse; for instance, user may make a mode select on an embodiment ofapparatus 100 that is in the form of a handheld weapon and/or objectthat determines a response when they pull a trigger or otherwise deployresponses.

With continued reference to FIG. 1 , behavior descriptor determinationmay be performed automatically. Determination may be performed byreference to an authorization database 144. Authorization database 144may be implemented, without limitation, as a relational authorizationdatabase 144, a key-value retrieval authorization database 144 such as aNOSQL authorization database 144, or any other format or structure foruse as an authorization database 144 that a person skilled in the artwould recognize as suitable upon review of the entirety of thisdisclosure. Authorization database 144 may alternatively or additionallybe implemented using a distributed data storage protocol and/or datastructure, such as a distributed hash table or the like. Authorizationdatabase 144 may include a plurality of data entries and/or records asdescribed above. Data entries in an authorization database 144 may beflagged with or linked to one or more additional elements ofinformation, which may be reflected in data entry cells and/or in linkedtables such as tables related by one or more indices in a relationalauthorization database 144. Persons skilled in the art, upon reviewingthe entirety of this disclosure, will be aware of various ways in whichdata entries in an authorization database 144 may store, retrieve,organize, and/or reflect data and/or records as used herein, as well ascategories and/or populations of data consistently with this disclosure.Authorization database 144 may include one or more entries tied toidentification of subjects 308, for instance as shown in Table 1 below.One or more entries may, for instance, include data entered in fieldsfor a name, position within an organization, threat categorization,authorization level, or the like; additional entries and/or tables mayindicate precisely which actions a subject 308 is permitted to engagein, times of day at which a subject 308 is permitted to be in subjectarea and/or to engage in a given activity, whether subject 308 is“blacklisted” and/or treated as a threat per se based solely on thesubject 308's identity, whether the subject 308 is in a “friend file” orlisting of persons for whom no security response is warranted, or thelike. For instance, and without limitation, where apparatus 100 is ahome security system, residents of the home and/or persons added to afriend file thereby may never be treated as having any behaviordescriptor, and thus apparatus 100 may take no deterrent action againstsuch persons. As a further non-limiting example, an employee currentlyon a shift at subject area may be authorized to be there and receive nodeterrent response. On the other hand, an employee who is in subjectarea outside of a shift, or who is not authorized to be specifically insubject area, albeit authorized to perform other actions or be in otherareas within a building that contains subject area, for example may betreated as a potential threat and may not be treated as exempt fromdeterrent responses, depending upon other threat determinations as setforth in greater detail below.

TABLE 1 Identifier Name Blacklist Friend File Shift Security Level4239945 Gerald Smith N Y — 5 9850059 Jane Doe Y N — — 8709855 Ann Salk NN Morning 3

Alternatively or additionally, apparatus 100, processor 136, and orremote device 140 may perform behavior descriptor determination byreference to a decision tree 148 which evaluates behavior descriptorsbased upon inputs from imaging device 104 and/or other sensors bysubjecting such behavior descriptors to a hierarchical set ofevaluations represented as decision nodes to determine at leaf nodeswhether a given behavior descriptor applies to subject 308.

Referring now to FIG. 8 , an exemplary embodiment 800 of decision tree148 for threat determination is illustrated. Decision tree 148 mayinclude a plurality of internal nodes 804 that evaluate one or moretests to determine behavior descriptor. Decision tree 148 may include aplurality of leaf nodes 808 corresponding to behavior descriptordeterminations and/or determinations of responses to perform based onbehavior descriptors. Processor 136 may traverse decision tree 148 onceand/or iteratively, with each iteration potentially arriving atdifferent behavior descriptors depending on additional data collected,outcomes and/or determinations of each previous iteration, or the like.For instance, and without limitation, if a first iteration of decisiontree 148 indicated a first behavior descriptor, a second iteration withno change in data from sensors and/or imaging device 104 a certainperiod of time later may cause an increase in behavior descriptor. As afurther example, if an initial iteration determines a first behaviordescriptor sufficient to warrant a first response, and a seconditeration occurs after the first response, the second iteration mayescalate the behavior descriptor if the first response produced nochange in data concerning subject. Alternatively, subject may not berecognized on a first iteration, but a second iteration may identifysubject, which may reduce behavior descriptor for a friend-listedsubject and/or a subject authorized to be present, while behaviordescriptor may be increased for a blacklisted subject. Each iterationmay further evaluate position, proximity to item to be protected and/orapparatus 100, whether subject is armed, or the like.

With further reference to FIG. 8 , apparatus may identify and/or tracksubject by detecting and/or interacting with an electronic device,characteristic, object or other defining characteristic of and/or on theperson of subject. For instance, and without limitation, may send asignal to a subject's phone leaving a breadcrumb or other tracking datumsuitable for later identification thereof. Identification of the subjectdevice may alternatively or additionally include fingerprinting asubject device; this may be performed as a function of at least a fieldparameter of at least a communication received therefrom. At least afield parameter may be any characteristic and/or specific value set by asubject device and/or user thereof for any exchanged data or phenomenaaccording to protocols for electronic communication or intrinsiccharacteristics or capabilities of the subject and/or device. As anon-limiting example, the International Mobile Equipment Identity (IEMI)number may be detected to identify a phone a person is in possession ofto recognize the person or data communications and data fields passedwith a network using HTTP from the phone may be used to identify orfingerprint the phone. Additional fields that may be used may includebrowser settings such as “user-agent” header of browser,“accept-language” header, “session_age” representing a number of secondsfrom time of creation of session to time of a current transaction orcommunication, “session_id,” “transaction_id,” and the like. Determiningthe identity of a subject device may include fingerprinting a subjectdevice as a function of at least a machine operation parameter describedin at least a communication. At least a machine operation parameter, asused herein, may include a parameter describing one or more metrics orparameters of performance for a computing device and/or incorporated orattached components; at least a machine operation parameter may include,without limitation, clock speed, monitor refresh rate, hardware orsoftware versions of, for instance, components of a subject device, abrowser running on a subject device, or the like, or any otherparameters of machine control or action available in at least acommunication. In an embodiment, a plurality of such values may beassembled to identify a subject device and distinguish it from otherdevices of one or more remote devices.

Apparatus may alternatively or additionally perform communication withsubject via subject device, both when subject is in subject area andthereafter. For instance, apparatus may call or message subject viasubject device to provide warnings or instructions thereto, and/or tofollow up with communications, warnings, summonses, or the likeregarding a past encounter.

Still referring to FIG. 8 , decision tree 148 evaluations of behaviordescriptor may depend on whether subject is in friend file; a levelcorresponding to no threat and/or no response warranted may correspondto an identification of subject in friend file. Processor 136 maydetermine an authorization level of subject 308 as a function of apersonal identity of subject, such as without limitation anauthorization level of a subject who is an employee permitted to be insubject area for certain periods of time, or the like. Decision tree 148evaluations of behavior descriptor may depend on a distance to subject;for instance, where subject is far from item to be protected and/orapparatus 100, a behavior descriptor may be moderate, warranting awarning or the like; where subject is at an intermediate distance,behavior descriptor may be raised to medium indicating a need for aslightly elevated response as described in further detail below, andwhere subject is close to apparatus 100 and/or item to protect, behaviordescriptor may be raised to a higher level corresponding to a moreaggressive response. As a further non-limiting example, processor 136may be configured to detect a behavior of the subject 308 and determinea behavior descriptor as function of behavior, where aggressive behaviormay correspond to a high behavior descriptor while less aggressivebehavior may correspond to a lower behavior descriptor; degree ofaggression of behavior may depend, without limitation, on adetermination by a behavior classifier 448 as described above.

With continued reference to FIG. 8 , processor 136 may be configured toidentify an object in possession of the subject 308 and determine thebehavior descriptor as a function of the object; identification ofobject may be performed without limitation using tool classifier 440.For instance, and without limitation, an object in control of subjectthat is classified as a weapon and/or tool used in breaking and entry,vandalism, or the like may map to a high behavior descriptor, an objectthat could be used for innocuous or malicious actions may map to amedium or intermediate behavior descriptor, and an innocuous object suchas a sandwich, coffee cup, or the like may not increase behaviordescriptor at all. Behavior descriptor determination may further dependon a time of day; for instance, all behavior descriptor determinationsmay be increased by one or more levels or quantities at night, during atime when subject area is closed to the public, or the like. Behaviordescriptor determination may also depend on an alert status, which maybe set, without limitation, by a user input or a transmission from aremote device 140; for instance, where nearby civil unrest, a crimewave, a disaster, or the like has caused an elevated risk of maliciousor otherwise damaging activity, alert level may be raised, which maycause behavior descriptors determined as described herein to be greaterthan they otherwise would be.

Still referring to FIG. 8 , behavior descriptor determinations may bebased upon any combination of elements described in this disclosure. Forinstance, an object on and/or held by subject may have a higher behaviordescriptor when coupled with an aggressive behavior and/or a previousdisregard for a warning and/or deterrent action. As a further example, atime of day corresponding to nighttime and/or a time at which the publicare not expected in subject area may increase a behavior descriptorassociated with a tool that could have either innocuous or maliciouspurposes, such as a tool suitable for aiding in vandalism or theft,which may get a higher behavior descriptor if used after dark.

With continued reference to FIG. 8 , alternative or additional objectsand or processes may be used in place of, or in combination with, adecision tree 148. For example and without limitation, a supervisedmachine learning model representing a linear equation or othermathematical relationship combining various parameters which may bereceived from imaging device 104, and or other sensor inputs, and orwhich may be developed using processes disclosed in this disclosure, maybe used to calculate behavior descriptors. A supervised machine learningmodel of this kind may be developed using training data that associatesbehavior descriptors with different sets of parameters, for instance asinput by users, and or as developed in previous iterations of methods asdescribed in this disclosure. Supervised machine learning model may begenerated using any supervised machine-learning process as describedabove. As a non-limiting example, machine-learning models may be used togenerate nodes of decision tree.

Still referring to FIG. 8 , behavior descriptors may be associated withactions or states of entrance other than Those associated with maliciousbehavior, such as accidental trespass, and our entry into a locationwhile suffering from an infectious disease, such as a disease that iscurrently part of an outbreak. For instance, behavior descriptor may bedetermined based upon body temperature, as computed according toprocesses described in this disclosure.

With further reference to FIG. 8 , behavior descriptor determination maybe performed recursively and or iteratively. For instance, and withoutlimitation, processor 136 may be configured to perform decision tree 148processes and or other processes for threat determination repeatedly inresponse to new parameters and or data being received from imagingdevice 104 and or sensors, in response to passage of time, as a regularpolled process, and/or in response to deterrent actions and or responsesthereto by subject. Persons skills in the art, upon reviewing theentirety of this disclosure, will be aware of various alternative and oradditional ways in which threat determinations may be performed onceiteratively recursively or like, as consistent with this disclosure.Each such variation is within the scope of this disclosure.

Referring again to FIG. 1 , apparatus 100 includes a deterrent component152 communicatively connected to processor 136. A “deterrent component,”as used in this disclosure, is a component and/or device configured togenerate a non-lethal deterrent at subject 308. A deterrent may includeany physical or psychological interaction with subject 308 thatdiscourages and/or stops subject 308 from performing a behavior contraryto objectives of apparatus 100.

With further reference to FIG. 1 , in an embodiment, and withoutlimitation, deterrent component 152 may include one or more non-lethaldeterrents for vermin and/or species external to Homo sapiens. Deterrentcomponent may include a directed light deterrent 156. A “directed lightdeterrent,” as used in this disclosure, is a deterrent that uses ahigh-intensity light source, such as, but not limited to, a laser, superLED, laser illuminated LED, super-luminescent LED, EELD, VCSEL, plasmadischarge lamp, and/or high-intensity LED that is actively aimed atand/or focused on subject 308, to generate a deterrent effect. In anembodiment, deterrents such as without limitation directed lightdeterrent may be used to blind or otherwise attack snakes. This mayinclude permanent damage for non-human animals. Where retroreflection,as defined and described in further detail below, indicates a signatureof a species to be exterminated, such as invasive species in thewildlife, deterrents and/or lethal devices may be used to blind orotherwise damage them. In an embodiment, apparatus may use a classifierto identify species of animal; for instance, any classifier as describedin this disclosure may be trained using training examples correlatinguser-entered identifications of animals with images thereof, which maybe used to identify image data of animals and/or classify such images toparticular animal species.

Referring now to FIG. 9 , an exemplary embodiment of a finite statemachine (FSM) 900 that may execute on apparatus and be used to determinea deterrent action to be performed by apparatus 100 is illustrated. Insome embodiments, an initial watch state 904 may function as a defaultstate of FSM 900. Watch state may include a state in which apparatus 100uses sensors to check whether a subject has entered subject area. Duringwatch state, apparatus 100 may scan a subject area with light radardevices, listen on and/or poll sensors as described above, or the like.Upon detection of a subject in subject area, FSM 900 may proceed to atrack state 908, in which the apparatus monitors movements of thesubject, for instance and without limitation to determine whether thesubject is in an unauthorized location, is behaving in one or moreundesirable ways as described in data store in apparatus, or the like.In track state 908, apparatus 100 may additionally determine whethersubject is on a “friend list” or otherwise is permitted to be in subjectarea and/or to perform one or more actions that may not be permitted topersons not authorized, whether subject is blacklisted due to previousactivities and/or encounters, whether subject has been previouslymonitored and/or interacted with at another apparatus, sensor, or otherdevice connected to apparatus 100 via a communication network, or thelike. If any trigger action takes place, FSM may move to an escalatedstate from track state 908; which escalated state follows may depend ontrigger actions detected. Escalated states may include a warning state912, in which, for instance, a directed light deterrent may be deployedon a chest of subject, a warning message may issue, or the like. Anadditional escalated state may include an interdiction state 916, inwhich one or more deterrent outputs are generated; in an embodiment,outputs in interdiction state may depend on any determination regardingthreat levels, types of behavior, data concerning likely efficacy of anygiven deterrent and/or deterrent mix, or the like. Triggers, threatlevels, types of behavior, data concerning likely efficacy of any givendeterrent and/or deterrent mix, or the like may cause different outputsat a given state and/or transfer to another state associated with adifferent output. Escalated states may include a hide and seek state920, in which hide and seek procedures using directed light deterrent,as described in this disclosure, may be employed. De-escalation triggersmay cause return to track state, warning state, and/or watch state.

With continued reference to FIG. 9 , apparatus may be paired and/orcombined with one or more elements of signage and/or warning systemsand/or labels to inform subject that an area is secured. Apparatus mayoutput a plurality of warnings, which may escalate in tone, intensity,vocabulary, or the like, to ensure that subject is given sufficientnotice of possible trespass and/or interdiction prior to use ofdeterrents. Warnings by apparatus may be calibrated to sufficientlyidentify and/or put subject on notice of deterrents to comport withlocal regulation concerning notice prior to use of force.

Still referring to FIG. 9 , in an embodiment, one or more escalationtriggers that may cause a modification of states and/or outputs togenerate a more aggressive deterrent may include a reduction in distancefrom apparatus 100 and/or an object, person, and/or area to be guarded,an amount of time spent in an unauthorized area, a speed, velocity,acceleration, and/or direction of movement of subject, time spent at agiven speed, and/or any threat level and/or behavioral determination asdescribed in this disclosure. De-escalation triggers may include,without limitation, modifications to less threatening behavior,reduction in determined threat level, compliance with instructionsissued from apparatus 100, departure from subject area and/or anunauthorized area, reduction in speed, velocity, acceleration, and anyother action that would occur to any person skilled in the art uponreviewing the entirety of this disclosure.

Referring now to FIG. 10 , an exemplary embodiment 1000 of a directedlight deterrent 156 is illustrated. Directed light deterrent 156 mayinclude a light source 1004. Light source may include, withoutlimitation, one or more elements of a laser, such as an electrical oroptical pumping source, an amplifier, and or one or more beam shapers,homogenizers, directors, filters, focus elements, expanders or the like.Light source may alternatively or additionally include a super LED,laser illuminated LED, super-luminescent LED, EELD, VCSEL, plasmadischarge lamp, and/or high intensity LED, which may be used directly,and or as an optical pumping source. Directed light deterrent 156 mayinclude a photodiode 1016 either integral to the light source 1004, orexternal to the light source 1004 with the use of a beam splitter 1008or similar optical device that directs a fraction of the outputted lightenergy as a sample beam 1012 to the photodiode 1016 or similar sensingdevice for determination of laser safety, efficiency, power use, and/orother critical parameters of the light source. Directed light deterrent156 may include an optional shutter 1020 or similar device thatinterrupts the outputted light source separate for the direct powercontrols of the light source 1004. This shutter 1020 may provideenhanced safety characteristics for the device as a backup and/orfailsafe deterrent interruption based on operating parameters of theapparatus 100. Directed light deterrent 156 may include a beam expander1024. Beam expander may convert a laser beam or other light sources usedin this disclosure of having a first width to an output beam having asecond width. The beam expander may be a set power or be variable innature being controlled electronically or mechanically to vary theexpansion or focusing effect of the light beam. Directed light deterrent156 may include a free beam spreader 1028. A beam spreader 1028 maycause the beam to diverge to a greater extent than if it were not passedthere are beings better period beams better may include, withoutlimitation, a lens which is concave with respect to the outwardtransmission of light. Alternatively or additionally, beam spreader mayinclude any refractive, diffractive, and or reflective element which hasthe effect of causing greater diversions of a beam. Directed lightsource may include one or more focusing optics 1032 a-b one or morefocusing optics 1032 a-b may include two or more refractive elementshaving inability to change a focal point of a beam passing through twoor more refractive elements that may be separated by a distance, whichmay be variable. For instance, two or more optical elements may beseparated by a servo controlled, liquid/polymer variable optic, voicecoil motors/actuators and lens, piezo motors and lens, and/or MEMScontrolled adjustable distance, which an electronic device, such asprocessor 136, and or driving electronics as described in further detailbelow modify a focal point of an outgoing beam. Directed light deterrentmay be configured, without limitation, to modify an energy density oflight output from directed light deterrent based on distance to atarget; distance may be determined, without limitation, using ToFcalculations, parallax, measurement and/or comparison of relativeapparent size of subject, or the like. Energy density may be modified,without limitation, to ensure equivalent momentary and/or total energydelivery at all distances. Energy density may be modified using beamexpanders that are configured to change a beam width rapidly.Calibration of directed light deterrent aiming may be done with eachlevel of beam expander to correct for distortion, differences indirection of beam, or the like that may occur at different degrees ofbeam expansion by measuring beam direction using, e.g., cameras atdifferent beam expansion levels and determining corrective biases and/orfactors for aiming coordinates from divergences from intendedcoordinates that are observed.

Audio system may be used to determine safe audio levels at differentdistances in a given space. When apparatus 100 starts to broadcastaudio, sound picked up by microphones can be compared to distancesand/or dimensions as measured for instance using time of flightcalculations; this may then be used to determine whether sound deliveryis at or above 140 dB or another set limit for the target zone.

Still referring to FIG. 10 , directed light deterrent 156 may include abeam steering component, which may consist of, but not limited to, twoor more reflective elements used as scanning mirrors, spatial lightmodulators, metamaterials/metasurfaces, liquid crystal directors, Risleyprisms, microoptical arrays, fast steering mirrors, tip/tilt optics,holographic phase modulators, and/or off-centered lens elements. In oneembodiment, reflective elements, which may include any reflectiveelements for use in scanning mirrors as described above in reference tolight radar component 116, may be arranged in close proximity to oneanother on axes that are substantially orthogonal causing one mirror toact as a vertical scanning mirror 1036 and another mirror to act as ahorizontal scanning mirror 1040. Such an arrangement may enable rapidscanning of laser and or other light beams across objects in subjectarea. Directed light deterrent 156 may include any additional opticssuitable for use in optical instruments such as lasers or other highintensity light sources, including additional amplifiers, beamexpanders, or the like. In an embodiment, a beam may be collimated, ormay not be collimated at one or more different stages in its processingby optical instruments within directed light deterrent 156. Light fromdirected light deterrent may be coherent or may not be coherent,depending on desired applications. In some embodiments, optical elementsthrough which a beam may pass in directed light deterrent 156 may havean effect of dissipating, polarizing, wavelength shifting, filtering,modifying, homogenizing, interrupting, or spreading power of the beam.As a result, a beam incident on objects in subject area 300, includingwithout limitation a face or eyes of a subject, may have substantiallylower intensity than at initial production of the beam.

Still referring to FIG. 10 , directed light deterrent 156 includes aplurality of deterrent modes, each deterrent mode corresponding to adistinct deterrent action. For instance, directed light deterrent 156may include a first deterrent mode and a second deterrent mode, and maybe configured to perform a first deterrent action on the subject 308when in the first mode and a second deterrent action on the subject 308when in the second mode, where the first deterrent action is distinctfrom the second deterrent action. Deterrent actions that may beperformed in corresponding deterrent modes may include a “startle”action or rapid flash across the eyes of subject 308, which may surpriseand/or warn subject 308 of presence of deterrent action. In anembodiment, different wavelengths, pulse patterns, intensities, phases,polarizations, or combinations thereof may cause different degrees ofdiscomfort and/or emotional distress in subjects 308; for instance andwithout limitation, a blue wavelength may be more distressing whencombined with a green wavelength and alternatively pulsed at varyingrates than a single red and/or green wavelength, while persons may havea maximal sensitivity to intensity levels in a green wavelength.Additionally or alternatively, deterrent mode may include a “hide andseek mode”, wherein a “hide and seek mode,” as used herein, is a modethat initiates directed light deterrent 156 to pulse a light or othercountermeasure as a function of an external stimulus such as detecting aspecific landmark, characteristic, phenomenon and/or behavior. Forexample, and without limitation, hide and seek mode may denote that asubject hiding behind a post should be coerced and/or coaxed away frombehind the post prior emitting the pulsed wavelength of light to ensuremaximal effectiveness. As a further non-limiting example, hide and seekmode may include coaxing and/or coercing a subject to turn their head toreveal and/or expose their eyes, wherein directed light deterrent 156may emit a light targeted at subject's 308 eye. In an embodiment, “hideand seek” mode and other techniques may be used to frighten, startle,scare, or otherwise induce anxiety in a subject; this may make them moresusceptible to deterrence using deterrent devices. Further psychologicaltools, such as training a red dot on somebody's clothes to make thembelieve they are being targeted, may also be employed. In someembodiments, and without limitation, hide and seek mode may be combinedwith audio outputs such as text to speech and/or other modes, to createthe impression, for a subject, that the subject is interacting with aperson rather than a machine; audio outputs may include instructionsreacting to user behaviors, for instance and without limitation asdetermined by behavior classifier, enabling apparatus 100 to react toevasive maneuvers by subject with comments concerning such maneuvers, orthe like. Hide and seek mode may also be useful for determining when auser is not able to be targeted in general and/or specifically at theeyes by a light deterrent and/or directed light deterrent, such that,when a light deterrent is fired, exposure counts and/or aggregationsused to calculate Maximum Permissible Exposure (MPE), for instance asdefined in international laser safety standards, or the like may not addto the subject's accumulated dosage for a set time period.

Continuing to refer to FIG. 10 , deterrent actions may include a “glare”action whereby light source may illuminate retina of subject 308 for alonger period of time, either in a single apparently constant scanningoperation or in a series of distinct pulses, intensities, patterns, orother variations than in the startle action, but may not generate anafter-image or cause short-term impairment of vision after exposure;glare action may interfere with vision during glare action, which maycause a temporary cessation of activity by subject 308, cause subject308 to cover eyes, and/or create a sensation of discomfort in subject308 tending to discourage subject 308 from further activity.

In an embodiment, and still referring to FIG. 10 , glare may be causedby an excess of total irradiation and/or by excessive luminance range.“Disability glare” as defined herein causes reduction in visibility, atleast in part due to “veiling glare” characterized by an inability todistinguish contrast in a field of vision, relative to usual ability, asa result of the glare light source. “Discomfort glare,” as used in thisdisclosure, causes an annoying and/or painful sensation. Reduction invisibility may be attributable to light scatter in the eye. A magnitudeof disability glare may be estimated according to veiling luminanceL_(v) according to the following equation:

$L_{v} = {{9.2}{\sum\limits_{i = 1}^{n}\frac{E_{i}}{\theta_{i}\left( {\theta_{i} + {1.5}} \right)}}}$

where E_(i) is illuminance from an i^(th) glare source and θ_(i) is anangle between a target to be tracked by the person experiencing theglare and the i^(th) glare source. Disability glare may be proportionalto a “luminance contrast” C between target luminance L_(t) andbackground luminance L_(b), as expressed by the following equation:

$C = \left( \frac{L_{t} - L_{b}}{L_{b}} \right)$

C may be further characterized in terms of L, by:

$C = \left( \frac{L_{t} - L_{b}}{L_{b} + L_{v}} \right)$

Discomfort glare for a given light source having illuminance E and angleof incidence to a target θ may be quantified by:

$W = {5 - {2{\log\left( \frac{E}{{0.0}2\left( {1 + \sqrt{L/0.04}} \right)\theta^{0.46}} \right)}}}$

or a similar equation. In an embodiment, readaptation to lower lightlevels after glare exposure may take time, and visual performance may bereduced during a readaptation period. Glare may generally be a functionof parameters which may include, without limitation, illuminance at aneye, an angle from the eye of a glare source, luminance and/or size ofthe glare source, spectral power distribution of the light, and/orduration of an experience of illumination. Environmental parameters thatmay affect visual performance in presence of glare may includeenvironmental conditions such as ambient conditions and/orcomplexity/difficulty of a location, as well as parameters pertaining tosubject such as age and/or visual health. Glare may have a greaterdisabling effect for detection of harder to see targets. As a result, inan embodiment, an object to be protected using embodiments of system 100may be darkened, disguised, and/or camouflaged where glare from lightdeterrents is employed as a deterrent mechanism.

With continued reference to FIG. 10 , parameters affecting visualperformance after exposure to a light source may include illuminance atthe eye, duration of exposure, total irradiance or “dose” experiencedduring exposure, ambient light levels, age, and visual health. Increasedilluminance, veiling luminance, duration, and/or irradiance at the eyemay increase recovery time, thus increasing the period of impairment,while youth, higher ambient light, and/or visual health may decreaserecovery time. Similar parameters may also affect discomfort from glare,as well as experience dealing with glare and/or light deterrents, whichmay heighten coping mechanisms and/or resistance to discomfort.

In some embodiments, and still referring to FIG. 10 , apparatus may makeuse of one or more methods to determine when a subject is lookingdirectly at a directed light deterrent, or in other words when directedlight deterrent is able to strike the subject on a fovea of one or moreeyes. Such targeting methods may be selected to limit data needed toprocess large resolutions that are needed to cover the targetingprocesses, e.g. by using a computationally less intensive process and/ora process that requires less resolution; moreover, if eye location canbe determined precisely, it may be possible subsequently to restricttargeting and tracking to a subject's face and/or eye box. Targeting andtracking processes may be configured to ensure having a high enoughangular resolution of imaging devices to meet minimum range requirementsso as to have enough “pixels on target” at a given range. Sensors may beused singly or in combination, along with artificial intelligence,machine vision, machine learning, image processing, or the like asdescribed in further detail herein to first find a subject, then analyzethe facial area specifically for tracking and/or targeting purposes.

Still referring to FIG. 10 , eye detection algorithms may includemethods that involve retro reflection, which may use ambient and/orillumination light as described in this disclosure to purposely cause aretro reflection from a person's retina or other structure regardingtheir eyes, including contacts, glasses, eye lids, sclera, fundus, orthe like. In an embodiment, if an illuminated spot on the retina acts asa secondary source of light, light that is scattered from that spot andreturns back through the front of the eye may exit in the same directionit entered, headed back toward a light source, which may include adistant light source. In many nocturnal vertebrates, the retina has aspecial reflective layer called the tapetum lucidum that acts almostlike a mirror at the backs of their eyes. This surface reflects lightoutward and thereby allows a second chance for its absorption by visualpigments at very low light intensities. Tapeta lucida produce thefamiliar eye shine of nocturnal animals; this effect may enhancedetection of a non-human subject and/or eyes thereof. Humans do not havethis tapetum lucidum layer in their retinas. However, a sufficientlybright light such as a flash or other intense, albeit potentially brief,illumination may cause a reflection off of a human retina, colored redby blood vessels nourishing the eye.

In an embodiment, and still referring to FIG. 10 , apparatus may use anywavelength described in this disclosure, including without limitation850 nm light, to cause a retro reflection. A resulting image may then beprocessed for a “cateye” effect. Improvements for a signal to noise ofapparatus to use in sunlight, around artificial light, or the like mayinclude, without limitation, using light sources with very tightemission wavelengths, for instance and without limitation as describedelsewhere in this disclosure, in addition to matching optical filters onthe cameras to eliminate other light sources or significantly minimizethem, “photonic starvation” and/or other methods to manipulate thesignal to maximize it against the scene's background. Apparatus mayalternatively or additionally use polarization or other optical filtersto take advantage of polarization of light retro reflected off theretina, for instance by selectively detecting and/or analyzingcircularly or other polarized light from eyes of subject, which mayattenuate light reflected by subject's face, clothes, and the like whileletting the polarized light from the retro pass, greatly improvingsignal to noise ratio. As a non-limiting example, A focusing eye may actas a high-performance retroreflector, potentially appearing millions oftimes brighter when illuminated from a distance than would a matte whitesurface of the same area. As another non-limiting example, ocularretroreflection may produce circularly polarized light, circularpolarizing filters on the sensors may be used to further enhanced thesignal to noise ratio of the image.

Still referring to FIG. 10 , retroreflection may be performed using anyillumination and/or interdiction source described in this disclosure. Insome embodiments, a maximum of 880 or 900 nm may be used as an upperwavelength in retro reflection. Apparatus may perform retroreflectiondetection using two different wavelengths such as without limitation 850nm and 940 nm which may light skin similarly, but produce a differentretro reflection phenomenon. These differing images may be combined suchthat the retro reflection is isolated versus the illuminated surfaces.The apparatus may use VCSELs, and/or other lasers or other light sourcesfor illumination, which may be covert to humans, animals, and/orspecific imagers and/or sensors. In an embodiment, apparatus may employan “illumination” system for scanning, measuring, and/or observingsubject area and/or subject, and a separate “interdiction” system foruse as a light deterrent.

Further referring to FIG. 10 , one or more eye movement and/or detectionmethods may alternatively or additionally be employed. Methods mayinclude corneal reflection detection, which may utilize a reflection ofa beam of light from various surfaces of an eye the beam crosses. thebrightest reflection being at the outer corneal surface, known as afirst Purkinje image with second, third and fourth Purkinje images beingdimmer and corresponding respectively to the inner surface of the corneaand the outer and the inner surfaces of the lens. A reflection from anouter surface of the cornea and an inner surface of the lens are the twothat are utilized.

Another technique employed, and still referring to FIG. 10 may includelimbus tracking which detects a sharp boundary between the dark eyes andthe white sclera (the limbus) which can be easily detected optically asto an identifiable edge, using any suitable edge detection process suchas without limitation canny edge detection. Another technique mayinclude measurement of ellipticity of a pupil which may vary fromcircular, as it is viewed head on, to elliptical as it rotates away froman axis upon which it is viewed. An additional technique may include amovement measurement based on a head and/or eyes being moved together orsingularly, using anatomical landmarks, image classification, or thelike. An additional technique may include use of and/or emulation of anoculometer, which may determine a center of the pupil and a cornealhighlight from a reflected light and the change in a distance anddirection between the two as an eye is rotated.

In an embodiment, and still referring to FIG. 10 , an eye image may beproduced by illumination of a face with light from a light source, whichmay include any light source described herein including withoutlimitation a near infrared light emitting diode source positioned out offocus at a center of a camera lens. Eye image may be captured and/oradded to a video frame image all or part of a face, and may includewhite and iris portions (dark), the back reflected infrared light out ofthe pupil (bright eye effect) and the corneal reflection of the source(glint). Eye gaze may be computed at relative x,y coordinates of theglint and a center of the pupil determined from an eye image usingpattern recognition software. Determination of the thresholds between apupil and surrounding eye portions and between a high intensity glintand surrounding eye portions may be performed using a histogram analysisof pixel intensities near the pupil and glint. An eye image region maybe identified within an overall frame image by a search for a maximumpixel intensity due to a corneal glint.

Still referring to FIG. 10 , a source of illumination employed for eyeand/or gaze detection may include, without limitation, an infrared lightemitting gallium arsenide diode or the like, which may emit light in aninfrared region at 880 nm, 900 nm, 905 nm, or the like. Light used maybe invisible and of a sufficiently low intensity that it is perfectlysafe for continued use. A near infrared wavelength of 880 nanometers mayaccomplish the invisibility and safety parameters. Any exposure toelectro-magnetic or other radiation will be applied to the subject'smaximum permissible exposure when safety aspects are calculated by thesystem.

In an embodiment, and with further reference to FIG. 10 , a frame thatis grabbed may be plotted as to an intensity of each individual pixelinto a pixel intensity histogram. Histogram may include one or morepoints of higher intensity representing a high intensity glint and/orretinal retroreflection, which may be used to determine a direction ofgaze. A histogram may alternatively or additionally be input to amachine-learning algorithm trained using training data correlatinghistograms to gaze directions; in this way, histogram data correspondingto more distant faces may be used to estimate and/or determine a gazedirection.

Still referring to FIG. 10 , Purkinje Image-based glint detection mayalternatively or additionally used to detect eyeglasses, safety goggles,or other surfaces generating specular reflections.

Alternatively or additionally, and with continued reference to FIG. 10 ,detection may be model based, for instance and without limitation usinga shape of a cornea, sclera, or the like to determine a direction ofgaze; models may include models trained using machine learningalgorithms as described in this disclosure, and/or models based on oneor more predictions of light intensity and/or shapes detected usingcomputer vision. In an embodiment, an eye of a subject may have one ormore curvatures that may be used for determining a model of the eye fromremote 3D imaging, such as shape from polarization, LIDAR, syntheticaperture lidar, multiwavelength laser for interferometry, or the like. Alaser may use typical time of flight processing to find general range totarget, but may use several closely spaced wavelengths in the beam maythen be employed, permitting apparatus to use interferometric or otherphase processing techniques, use the results for extremely finelocalized ranging to within a few microns of structures. This may beused to confirm an object that has the depth of an eyeball on a targetfor confirmation that it is an eyeball.

Further referring to FIG. 10 , different pattern recognition techniques,such as template matching and classification, may be employed todetermine eye movement and/or gaze direction. For instance, and withoutlimitation, methods may include use of principal component analysis tofind a first six principal components of an eye image to reducedimensionality problems, which arise when using all image pixels tocompare images. A neural network or other machine-learning model maythen be used to classify a pupil position. Training data for a neuralnetwork and/or other machine-learning model may be gathered duringcalibration, where for instance a user may be required to observe fivepoints indicating five different pupil positions.

Still referring to FIG. 10 , an alternative or additional eye trackingsystem may use a particle filter which estimates a sequence of hiddenparameters depending on data observed. After detecting possible eyespositions, a process of eye tracking may commence. For effective andreliable eye tracking, a gray level histogram may be selected as thecharacteristics of the particle filter. In an embodiment, low-levelfeatures in the image may be used to reduce computational overhead for afast algorithm. An alternative or additional approach may use aViola-Jones face detector, which is based on Haar features, to locate aface in an image, after which template matching may be applied to detecteyes. Zernike moments may be used to extract rotation invariant eyecharacteristics. Support vector machine and/or other machine learningmay be used to classify images to eye/non-eye patterns. Exact positionsof the left and right eyes may be determined by selecting two positionshaving highest values among found local maximums in an eye probabilitymap. Detecting an eye region may be helpful as a pre-processing stagebefore iris/pupil tracking.

In other embodiments, and continuing to refer to FIG. 10 , eye gazedirection may be estimated by iris detection using edge detection andHough circle detection. Eye detection may alternatively or additionallybe performed using a sequence of face detection and Gabor, wherein aneye candidate region is determined automatically using the geometricstructure of the face; four Gabor filters with different directions (0,π/4, π/2, 3π/4) may applied to an eye candidate region. The pupil of theeye does not have directions and thus, it may be detected by combiningthe four responses of the four Gabor filters with a logical product.

In other embodiments, and continuing to refer to FIG. 10 , eye gazedirection may be estimated by iris detection using edge detection andHough circle detection. Eye detection may alternatively or additionallybe performed using a sequence of face detection and Gabor, wherein aneye candidate region is determined automatically using the geometricstructure of the face; four Gabor filters with different directions (0,π/4, π/2, 3π/4) may applied to an eye candidate region. The pupil of theeye does not have directions and thus, it may be detected by combiningthe four responses of the four Gabor filters with a logical product.

Still referring to FIG. 10 , eye movement may be analyzed in terms ofsaccades, fixations, and blinks. Saccade detection may be used as abasis for fixation detection, eye movement encoding, and the wordbookanalysis. In an embodiment, apparatus may first compute a continuous 1Dwavelet coefficients at scale 20 using a Haar mother wavelet. Forinstance, where s is one of these signal components and the motherwavelet, a wavelet coefficient C_(b) ^(a) of s at scale a and position bmay be defined

$C_{b}^{a} = {\int{{S(t)}\frac{1}{\sqrt{a}}{\varphi\left( \frac{t - b}{a} \right)}dt}}$

where the integral is evaluated over the infrared spectrum in question.By applying an application-specific threshold th_(sd) on thecoefficients C_(i)(s)=C_(i) ²⁰(s), further analysis may create a vectorM with elements M_(i):

$M_{i} = \left\{ \begin{matrix}{1,} & {\forall{{i:{C_{i}(s)}} < {- {th}_{sd}}}} \\{{- 1},} & {\forall{{i:{C_{i}(s)}} > {th}_{sd}}} \\{0,} & {\forall{{{i:} - {th}_{sd}} \leq {C_{i}(s)} \leq {th}_{sd}}}\end{matrix} \right.$

This step may divide detected motions into saccadic (M=1,−1) andnonsaccadic (fixational) (M=0) segments. Saccadic segments shorter than20 ms and longer than 200 ms may be removed. These boundaries mayapproximate typical physiological saccade characteristics. Apparatus maythen calculate an amplitude and direction of each detected saccade. Asaccade amplitude SA may represent a difference in motion signalamplitude before and after a saccade. A direction of motion may bederived from a sign of corresponding elements in M. Each saccade isencoded into a character representing a combination of amplitude anddirection. Humans typically alternate between saccades and fixations.Thus, detection of saccades may be used for detection of fixations. Analgorithm may exploit the fact that gaze remains stable during afixation. This may result in derivation and/or detection ofcorresponding gaze points. Fixations may be identified by thresholdingon a dispersion of these gaze points. For a segment S of length ncomprised of a horizontal and a vertical signal component, dispersionmay be calculated as

Dispersion(S)=max(s _(h))−min(s _(h))+max(s _(v))−min(s _(v))

Initially, all nonsaccadic segments may be assumed to contain afixation. Apparatus may then drop segments for which the dispersion isabove a maximum threshold of 10,000 or if its duration is below aminimum threshold of 200 ms.

Further referring to FIG. 10 . for blink detection, an algorithm may usea threshold on wavelet coefficients to detect blinks in a verticaldirection. In contrast to a saccade, a blink is characterized by asequence of two large peaks in a coefficient vector directly followingeach other: one positive, the other negative. Time between these peaksmay be smaller than the minimum time between two successive saccadesrapidly performed in opposite direction. This is because, typically, twosaccades have at least a short fixation in between them. For thisreason, blinks may be detected by applying a maximum threshold on thistime difference.

Further referring to FIG. 10 , gaze fixation of the subject may beestimated using one or more of measured or inferred measurements of thescene and/or subject. In an embodiment, if resolution is not high enoughto directly measure the subject's gaze fixation direction, the apparatusmay use the pose of the head and inferred human eye behaviors, such as,human's will usually keep their gaze to within ±20° of their centralvision cone, favoring smaller angles, before turning their heads. Theapparatus may infer if the subject's head is posed 5° off the directionof the apparatus, that the gaze is most likely within ±15° from thisposition and decide to engage the subject with optical countermeasures.

In an embodiment, and still referring to FIG. 10 , directed lightdeterrent may produce wavelengths that cause a subject ocular lens tofluoresce, which may cause glare even for directed light deterrent thatstrikes subject eye off-axis, missing the retina and/or fovea. Forinstance, wavelengths in the near-UV or short-visible wavelengths mayinduce a blue-green fluorescence, which may function as a source ofintraocular veiling glare. Wavelengths longer than a ˜365-nm lensabsorption peak may induce progressively weaker but also progressivelymore red-shifted fluorescence emission. A more red-shifted emission mayhave a higher luminous efficiency and result in an approximatelyconstant luminous efficiency when excited by equal radiant exposuresover the wavelength range from 350 to 430 nm. Perceived color offluoresced light depends on a wavelength of excitation light, with awavelength range of 390-410 nm associated with a green to yellowishgreen perceived fluoresced color, while excitation wavelengths inapproximate region of 430 nm may produce fluoresced light having bluegreen to purplish-red perceived colors. Perceived wavelengths maydepend, without limitation, on age of a subject. Lens fluorescence maycause glare in eyes of a person even based on incident or off-axisexposure to light, such that light that is not directly entering thefovea and/or pupil may still cause visual deterrent effects. In someembodiments, directed light deterrent may combine colors that causeocular lens fluorescence with other visible spectrum colors; in anembodiment, colors may be alternated using multiplexing mirror devicesas described in further detail below. Wavelength used may beapproximately 405 nm, which may be generated using a solid-state lasersuch as an EELD or a VCSEL.

In an embodiment, and with further reference to FIG. 10 , lasers and/ordirected light deterrents at different angles to a subject may emitdifferent wavelengths depending on relative angle to eyes, which may bedetermined using any processes and/or techniques described herein.green, red, blue or other visible wavelengths may be provided by on-axislight sources, while violet may be on the side, off-axis, for instancewith 405 nm fluorescing glare. In addition, where directed lightdeterrents are acting in concert, a subject may be unable to avoidlights by moving his or her head. This can have the effect of simulatingpresence of a larger force of persons and/or devices, acting as a “forcemultiplier.”

Still referring to FIG. 10 , deterrent actions may include an“after-image” action, which may cause some degree of occlusion of visionwith an after-image due to greater exposure to light than in “glare” or“startle” actions. After-image may interfere with effective use ofvision for some period after after-image action, impairing an ability ofsubject 308 to continue carrying out an action contrary to securityobjective. Duration of impairment may depend, without limitation, on awavelength used by directed light deterrent 156; for instance, a redwavelength may create a more lasting afterimage or other impairment thana blue and/or green wavelength. Accordingly, where apparatus 100 isbeing used in a mobile setting such as a hand-held and/or drone device,a red wavelength may be used to create longer-lasting impairment to helpa user in escaping from and/or subduing subject 308, while if areadenial or encouragement of subject 308 to leave is of interest, a bluewavelength may be used for shorter-duration impairment, permittingand/or enabling subject 308 to escape from subject area.

Further referring to FIG. 10 , deterrent actions may include, withoutlimitation, a “saturation” action, which may include a sufficientexposure to high-intensity light to saturate an optic nerve of subject308. Saturation may cause total and/or near total temporary impairmentof vision, and/or loss of short-term visual memory, where as before,blue light may create a shorter-acting impairment and red light maycreate a longer-lasting impairment. Saturation may also cause sensationsof pain, headache, and/or nausea, irritation, confusion, occasioningsevere discomfort in subject 308, and thus producing a strong deterrenteffect. In an embodiment, a glare action may transform an order ofmagnitude greater power to a retina of subject 308 than a startleaction, while a saturation action may deliver an order of magnitudegreater power to a retina of subject 308 retina than a glare action.

In an embodiment, and still referring to FIG. 10 , deterrent actions mayinclude a “strobe” action, in which light is pulsed against retina ofsubject 308 at a rate that causes discomfort and/or neurologicalimpairment, such as pre-epileptic effects. For instance, a visible lightsource may be configured to strobe at a rate between approximately eightHertz and approximately twenty-five Hertz, such as without limitation 12Hz. As a further non-limiting example, visible light source may beconfigured to strobe between two colors of light, one or both of whichmay excite cones in a retina which are not excited at all and/orsubstantially by the other wavelength; for instance, a first visiblelight source may generate a red wavelength followed by a second visiblelight source generating a blue wavelength—a pure red light may notexcite cones sensitive to blue/violet light. In an embodiment, andwithout limitation, strobing may induce a strobe effect. As used in thisdisclosure a “strobe effect” is a physiological response that occurs asa result of strobing. For example, and without limitation, strobe effectmay result in a physiological effect of differential and/or conflictingsignals being transmitted to the rods and/or cones of the retina of anindividual. In an embodiment, strobe effect may include one or moreeffects such as but not limited to Flicker vertigo, nausea,disorientation, seizures such as tonic and/or clonic seizures, “grandmal” seizures, “petit mal” seizures, absences, or the like. In anembodiment, and without limitation, strobing effect may be enhancedand/or mitigated as a function of a wavelength of the visible lightsource. For example, and without limitation, a red wavelength of thevisible light source may result in a greater epileptogenic effect.Strobing may be combined with any actions described above, includingwithout limitation glare, afterimage, and/or saturation actions.Alternatively or additionally, deterrent actions may include one or morerandom and/or pseudorandom pulse sequence generation processes, such aswithout limitation pulses of spacing and/or duration as generated usinga random number generator and/or pseudorandom number generator; randomand/or pseudorandom number generator may be implemented using anysuitable hardware and/or software implementation, including withoutlimitation linear feedback shift registers or the like. In anembodiment, and without limitation, random and/or pseudorandom pulsingmay have a disorienting and/or confusing effect, inducing anxiety orother psychological effects in a user. Additional effects produced mayinclude fanning light in an array creating a plurality of beams such asten beams, strobing left and/or strobing down, or the like. In someembodiments, behavior classifiers may be configured to detect entry intoa seizure state and/or one or other symptoms indicating sensitivity tostrobing; apparatus 100 may be configured to cease strobing effect andtransition to another deterrent output upon detection-detection ofstrobing sensitivity symptoms, such as any symptoms of physiologicaleffects described above, may be treated as a de-escalation trigger asdescribed above. In an embodiment, apparatus 100 may be configured toavoid triggering seizures that sufficiently incapacitate subject toprevent subject from leaving. In addition to seizure and/or near-seizureeffects, light may be used to generate flicker vertigo, sometimes calledthe Bucha effect, which may include an imbalance in brain-cell activitycaused by exposure to low-frequency flickering (or flashing) of arelatively bright light.

With continued reference to FIG. 10 , where apparatus 100 is networkedwith multiple other apparatuses having high-intensity light sources thatproduce different wavelengths of high-intensity light, and/or wheredirected light deterrent 156 includes light sources that producemultiple wavelengths of light, deterrent effect may include performingany or all of above-described deterrent actions using multiple and/orvaried colors. This may, in an embodiment, defeat attempts at eyeprotection by subject 308; for instance, and without limitation, whereeye protection has low transmittance at a first wavelength, it may havehigher transmittance at a second wavelength, for example so that subject308 is still able to see while wearing eyewear. In addition, visible,UV, and/or infrared wavelengths may be used to exploit weaknesses in theeyewear causing local fluorescing, temperature changes, and/or othereffects that would reduce the effectiveness of the eyewear without harmto the subject 308.

Further referring to FIG. 10 , apparatus 100 may be configured to detectcountermeasures by subject. Countermeasures may include, withoutlimitation, protective behaviors such as aversion of eyes, coveringears, crawling on the ground, using cover, or the like, protectiveequipment such as eye protection goggles and/or other eyewear, eyeprotection such as protective and/or noise-cancelling earphones and/orheadsets, or the like. Apparatus 100 may be configured to selectdeterrents to bypass countermeasures. Selection may be performed,without limitation, using a look-up table for behaviors and/or equipmentidentified using behavioral and/or image classifiers, using anadditional classifier and/or other machine-learning process, which maybe trained using training examples correlating countermeasures tosuccessful deterrents previously deployed against such countermeasures;training examples may be input by users. As a non-limiting example,where countermeasure blocks or otherwise avoids light deterrents,apparatus 100 may be configured to select and/or output anotherdeterrent such as an audio deterrent. Alternatively, a frequency of anaudio and/or light deterrent may be modified to circumvent protectionagainst other frequencies; for instance, and without limitation, where asubject is wearing eyewear that selectively reflects a first wavelength,apparatus may output a second wavelength that the eyewear does notselectively reflect. As a further example, where apparatus detectionshearing protection, apparatus may output a low-frequency sound, whichsubject may feel as a result of bone conduction or the like; in anembodiment, lower-frequency sound may also heighten a psychologicaleffect and/or “fear factor” from those lower-frequency sounds, even withhearing protection, because of the unsettling sensation of vibration inapparent silence. Apparatus 100 may alternatively or additionallyrandomize and/or vary deterrent payload until a desired reaction bysubject, such as compliance with instructions and/or retreat, isdetected.

In an embodiment, and still referring to FIG. 10 , deterrent actions mayinclude generating a visual impairment zone. As used in this disclosurea “visual impairment zone” is a barrier and/or area that is secured byan illuminated source. For example, and without limitation, visualimpairment zone may include an area and/or barrier that generates anilluminated barrier such that a subject that enters the area and/orinteracts with the barrier is susceptible to the light deterrent. Forexample, and without limitation, visual impairment zone may include anarea wherein no aiming control unit and/or targeting of a subject'sretina and/or eyeball is required.

Referring now to FIG. 11 , an exemplary embodiment 1100, directed lightdeterrent 156 may time emission to illuminate a vision band 1104 ofsubject 308. Vision band 1104 may be defined as a geometrical regionencompassing both eyes of subject 308. In an embodiment, directed lightdeterrent 156 may be configured to illuminate only vision band 1104, byblanking or switching off light transmission except when the beam ispointed at or scanning through vision band 1104. This may have an effectof adding to a surprise for the subject 308, as the subject 308 cannotsee the light shooting at various locations before the high intensitylight source finally makes its way to the vision band 1104. Timing oflight source activation and/or pulses may be synchronized with beamsteering and other modulation actions to pulse at a desired rate withinvision band 1104, while remaining off outside vision band 1104. Suchtiming may further illuminate vision bands 1104 of two or more subjects308 while maintaining the light source without activation at othertimes. In an embodiment, light intensity may have a first setting for afirst subject 308 and a second setting for a second subject 308,depending on distances of each subject 308 from light source and/orsources, for instance as determined in further detail below. Processor136 and/or other elements of apparatus 100 may track motion of subject308 head, foot, and/or leg movement, for instance using anatomicallandmark identification and tracking as described above, maintainingvision band 1104 centered about an axis halfway between subject 308eyes. Vision band 1104 may, for instance, be greater than 80 mm wideabout the axis 1108 connecting the subject's eyes, pertaining to theaverage interpupillary distance of humans of 64 mm to 80 mm. Wheremachine-learning processes are used to predict future movements ofsubject 308, placement of vision band 1104 may be directed according tosuch predictions; in an embodiment, this may enable more exact timing ofvision band 1104 illumination using pulses or the like. Apparatus 100may perform time multiplexing to set intensity, focal lengths, pulsewidths, pulse frequencies, wavelengths of light sources, and/or otherparameters for differently for two different targets. Time multiplexingmay be accomplished, without limitation, using one or more multiplexingmirror components as described in further detail below.

Still referring to FIG. 11 , directed light deterrent 156 may beconfigured to adjust power and/or intensity of light source according toone or more efficacy and/or safety considerations. Adjustment of lightsource power may be accomplished, without limitation, by means ofregulation of current to light source, regulating voltage to the lightsource, combing two or more light sources, using varying pulse widthmodulation and duty cycle, adjusting divergence and/or beam shape of thelight source, and/or by adjusting an amplifier to vary optical gain. Forinstance, power emitted by light source may be increased for a subject308 detected as having eyewear as described above. Where eyewear isfurther classified to identify categories of eyewear such as sunglasses,eyewear that protects against light, physical hazards, or the like,night-vision and/or infrared goggles, and/or visual corrective lenses,light intensity may be increased to counteract attenuative effect ofsuch eyewear; intensity increase may be limited according to one or moresafety limits as described in further detail below. Alternatively oradditionally, detection of eyewear may cause processor 136 and/orapparatus 100 to use non-optical deterrents instead of and/or incombination with optical deterrents.

In an embodiment, and still referring to FIG. 11 , adjustment of powerand/or intensity of directed light deterrent 156 may be performed asdirected by detected ambient light and/or detected state of subject's308 eyes, for instance, and without limitation, to eye color, pupildilation, retina retro-reflection, and/or other factors that affect thesubject's 308 sensitivity to light. For instance, and withoutlimitation, where apparatus 100 is mounted in a dark area, a lesserintensity may be used, while placement in an area with a higher degreeof ambient light may cause apparatus 100 to use a higher degree ofintensity. Detection of ambient light may be performed using an opticalcamera 108 as described above, for instance and without limitation bymeasuring luma and/or chroma values captured from ambient light insubject area. Setting of intensity and/or power level according toambient light may depend on an amount of time that apparatus 100 hasdetermined subject 308 has been present in subject area. For instance,it may take 20 minutes for a pupil to become dark-adapted after entryinto a dark room or other space from daylight. Apparatus 100 maytherefore configure directed light deterrent 156 to transmit at a highintensity for a subject 308 who has recently entered subject area duringdaylight hours. Where, as described in further detail below, apparatus100 is in communication with additional apparatuses in a mesh network orthe like, additional apparatuses may track subject 308 through two ormore subject areas, each having measured ambient light intensities;apparatus 100 may thus determine an ambient light level to which subject308 has become accustomed as a function of amounts of time subject 308has spent in subject areas, and may adjust intensity accordingly.

With further reference to FIG. 11 , the apparatus 100 may measure thecurrent state of the subject's 308 eyes, for instance, and withoutlimitations, using an infrared light to illuminate the subject's retinasand using a visible camera to measure the size of the pupils shown withretroreflected infrared light from the retina, measure the flux of theinfrared light that is retroreflected from the retina, and/or othermethods to determine the current dilation of the subject's pupils. Inaddition, a visible camera may image the subject's iris color to infersensitivity to light, as blue-eyed individuals are statistically moresensitive to light. The apparatus 100 may use an imaging device, suchas, but not limited to, a polarized camera or visible camera to detectthe relative glance angle of the subject with respect to the apparatus100 to determine off angle energy calculations for energy exposure orpredictive effects. In an embodiment, and without limitation, measuringthe current state of the subject's 308 eyes may include identifying aspecies. For example, and without limitation, identifying species of thesubject's eyes may include identifying one or more eye sizes, eyecolors, eye reflection colors, distance from a first eyeball to a secondeyeball, quantity of eyeballs present on subject 308, vibro-acousticalmeasurements, and the like thereof.

Still referring to FIG. 11 , apparatus 100 may be configured to detectsub-pixel signals indicative of phenomena of interest, such as withoutlimitation retinal retroreflection. For instance, and withoutlimitation, a classifier, which may include any classifier as describedin this disclosure, may be trained with images captured from asufficient distance that a retinal reflection occupies less than onepixel of resolution; such images may be correlated with imagessimultaneously or nearly simultaneously captured using a coaxiallypositioned imaging device that is closer to a picture subject, and whichthus has sufficient resolution to confirm retroreflection. This trainingdata may be used to train an image classifier that can identify retinalretroreflection at low relative resolutions within a given degree ofconfidence. Rapidly captured multiple images may be used to increasedegree of confidence past a preconfigured threshold level such thatdetection of retroreflection is essentially certain; this may in turn beused to determine orientation of a subject's head and/or to triggerdirected light deterrent when in “hide and seek” mode as described infurther detail below.

With further reference to FIG. 11 , apparatus 100 may use a phasedand/or multistep approach to detect eyes and/or other features usingretroreflection. For instance, and without limitation, apparatus 100 mayuse a light radar component, infrared detector, and/or imaging devicesuch as camera to detect a probable location of an eye, pair of eyes,other portion of anatomy containing eyes, and/or other objects to beretro reflected. Apparatus 100 may then rapidly illuminate a subjectarea with a burst of high illumination, which may be transmitted as arapid pulse, for instance and without limitation to minimize overallillumination despite high-intensity light. High intensity light may beproduced, without limitation, using one or more component lasers and/orother light sources of a light radar device, such as at least an EELD ora vertical-cavity surface-emitting laser (VCSEL). Timing of burst may becontrolled, without limitation, using q-switching components such aswithout limitation an electro-optical transducer, acousto-opticaltransducer, or the like; persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various alternative oradditional elements that may be used to control burst emission timing.

Still referring to FIG. 11 , light reflected from a pulsed burst asdescribed above, and/or other light to be detected in short bursts, maybe detected with APDs, SPADs, and/or other photodetectors, for instanceas described above. Such photodetectors may be gated, which may beaccomplished without limitation using any mechanism suitable forq-switching and/or gated release of light. A timing circuit, such as anasynchronous timing circuit, may time exposure of photodetectors tooccur during a brief window, such as a few milliseconds, during whichdetection may occur. Apparatus 100 may measure a flux of retroreflectedlight and/or other detected light using photodetectors. A bias ofphotodetectors may be adjusted to set a threshold for detection at someaverage threshold of photons. One or more filters, dichroic reflectors,or the like may be used to filter out one or more wavelengths of lightother than an expected wavelength to be detected, such as a wavelengthemitted to produce retroreflection. Signal to noise ratio may be furtherimproved by “photonic starving”-a detection window may be limited to avery short time to minimize influx of light other than an expectedsignal light. For instance, given a known and/or calculated distance toa user, an asynchronous circuit and/or very high-speed clocked circuitmay be set to time a camera shutter to pick up a retro-reflected photon;asynchronous circuit may be, e.g. triggered using a switching diodereverse biased using a capacitor and resistor circuit having a voltagein excess of reverse-bias voltage by an amount to be discharged by thecapacitor and resistor circuit during the time between emission andreturn of reflected photons so as to detect only at a moment of returnreflection. Such a circuit may alternatively or additionally be used toset and/or discharge a control terminal, such as a base or gate of atransistor.

In an embodiment, and further referring to FIG. 11 , emission of burstsand/or LIDAR may include emission of a pulse train rather than a singlepulse. Pulse train may be pulse code modulated, with a pulse codemodulated (PCM) code; apparatus may be configured to ignore returnsignals not including the code.

In an embodiment, and continuing to refer to FIG. 11 , light used forretroreflection may be pulsed at about 50 watts for approximately 5 ns.This may be performed using LIDAR and/or non-LIDAR laser sources at anysuitable wavelength, such as without limitation 805, 850, and/or 930 nm.

In an embodiment, and continuing to view FIG. 11 , once apparatus hasestablished eye tracking, it can be used to guide various otherprocesses and/or deterrents. For instance, and without limitation, eyelocations and separation may be used to determine locations of otherparts of the body. A thermal map may alternatively or additionally showdifferent areas of face. Thus, for instance, a microwave “pain ray” asdescribed in this disclosure could be aimed at soft and/or sensitiveareas of a subject's body, such as the vermillion, at lips, backs ofhands, other sensitive spots. Ears can also be located to performdirected and/or focused audio delivery to one or more ears to create adirectional effect. Retroreflection may alternatively or additionally beused to determine if a user is currently seeing a Morse codetransmitter. In an embodiment, where eye is not fixated, and/or isdithering, a beam of a directed light deterrent may be configured to belarge enough to compensate for traveling and/or dithering of eye.Directed light deterrent may alternatively or additionally dither toaugment and/or increase exposure levels and/or probability. In anembodiment, retroreflection data may be used to determine quickly ifsomebody is wearing eyewear, protective goggles, or the like.

Eye detection and/or retroreflection may be performed at largedistances. For instance, a beam at approximately 850 nm beam may be ableto produce detectable retroreflection up to 20 kilometers away; this maybe used to detect a subject at a distance. Some data may also beavailable at distance, such as a wavelength of retro-reflected light,and/or distances between eyes, which may be used to determine whatspecies subject belong to. In some embodiments, various VCSELs and/orlight output devices may be spread widely at high energy to detectretroreflection; upon detection, light output may be dropped down inenergy to cover the eyes more specifically, which may be used tocontinue eye tracking at lower energy outputs.

Still referring to FIG. 11 , alternatively or additionally to directedlight deterrent, an alternative light source, which may not be directed,may be used, such as one or more LEDs, SLEDS, laser-pumped phosphordevices, strobes, or other light-emission devices strobes or the like tobroadcast light.

In an embodiment, and still referring to FIG. 11 , apparatus may managepower density through variable beam expansion based proportionally todistance so that energy density is constant; for instance, and withoutlimitation, beam expanders and/or focusing optics may be used tomaintain a constant spot size on a target and/or subject regardless ofdistance. In an embodiment, this may keep energy density delivered tosubject at a constant amount in mW/cm² constant. Any other parameters ofdirected light deterrent may alternatively or additionally be varied,including without limitation frequency, pulse width, or the like.

With further reference to FIG. 11 , intensity and/or power levels ofdirected light deterrent 156 may alternatively or additionally be setaccording to one or more safety limitations. For instance, and withoutlimitation, intensity and/or duration of light output may be limited toless than maximum permissible exposure and/or some percentage less than100% of MPE, as determined as a function of beam intensity, beamdivergence, and distance from light source of subject 308. For instance,where light source is non-collimated and diverging, MPE for a subject308 at an opposite side of subject area may permit a substantiallyhigher intensity than MPE for a subject 308 who is near to light source.As noted above, intensity of light source may be varied according towhich subject 308 is being exposed to light. In an embodiment, a visiblelight source may be configured to shut off automatically where distanceto subject 308 is less than a preconfigured threshold amount. Distanceto subject 308 may be determined, without limitation, using ToFcalculation, object inference, stereoscopic vision, and/or other 3Dmeasuring techniques. MPE may be calculated, without limitation,according to ANSI 1504, ANSI 136.4, or other power exposure safetylimitations. MPE levels from directed light source may be measured atvarious power output levels using a power meter, to determine how MPEdepends on output intensity, distance to subject 308, and/or any otherparameters, permitting accurate safety determinations and/or computationof preconfigured distance threshold for shutoff. Power provided tosubject 308 may alternatively or additionally be determined usingreal-time feedback. For instance, power density in a target area such asvision band 1104 may be measured using chroma and/or luma valuescaptured at a wavelength of directed light deterrent 156; such feedbackmay be used to adjust intensity in real time and/or provided to remotedevice 140 for updated safety thresholds and/or firmware updates.Alternative or additional safety standards with which apparatus maycomply may include ANSI Z136.1-1974 for laser safety, NIOSH 1998 fornoise exposure (both continuous and impulse), and CFR 1910.95 (1971a,1983) and CFR 1910.95 (1971b) for noise exposure (both continuous andimpulse) in U.S. general industry and construction work, respectively.Basic metrics to be applied for exposure limits may include withoutlimitation Laser Maximum Permissible Exposure (MPE) in J/cm2 for lessthan 10 seconds (e.g., Table 5, ANSI, 2014), continuous noiseTime-Weighted Average (TWA) in decibels A-weighted (dBA) for an 8-hourequivalent exposure duration (e.g., NIOSH, 1998), and impulse-impactnoise in decibels peak (dBP) for sounds with dBP levels having maximumat intervals greater than 1 second (e.g., NIOSH, 1998). An outdoor lasersafety standard employed may include ANSI 136; an indoor laser safetystandard may include ANSI 134.

Still referring to FIG. 11 , in addition to MPE, safety determinationsand/or desired effects of directed light deterrent may be determinedusing maximum dazzle exposure (MDE); where MDE is calculated using asingle wavelength, apparatus may determine MDE for multiple wavelengthsand calculate it based on combined effective ratios of perceived MDE perwavelength. For example, if a 25-year-old person with brown eyesrequires 32 μW/cm² for a 520 nm laser and 234 μW/cm² for a 450 nm laserindividually for a dazzle effect to occur, apparatus may combine half ofeach, i.e., 16 μW/cm² of 520 nm and 117 μW/cm² of 450 nm laser power toachieve a comparable dazzle effect. This may be used to create morepsychological and physiological effects. These ratios may be based onthe MDE calculations and photopic, mesopic and/or scotopic responses ofhuman eyes in response to wavelengths.

In an embodiment, and still referring to FIG. 11 , MDE may be determinedwith respect to one or more equations and/or formulas. For instance,glare may be calculated as follows:

${g_{eye}\left( {\theta,A,p} \right)} = {\frac{10}{\theta^{3}} + {\left\lbrack {\frac{5}{\theta^{2}} + \frac{{0.1}p}{\theta}} \right\rbrack\left\lbrack {1 + \left( \frac{A}{6{2.5}} \right)^{4}} \right\rbrack}}$

where θ is an angle between the glare source and the line of sight(0.1°<θ<100°), A is an age of the individual (years), and p is an eyepigmentation factor (p=0 for black, 0.5 for brown, 1.0 for light, and1.2 for very light eyes). Two calibration factors, S₁ and T₁, may beapplied to eye scatter function as follows:

f _(eye)(θ,A,p,L _(b))=S ₁ L _(b) ^(T) ¹ g _(eye)(θ,A,p)

where L_(b) is the background luminance (cd·m⁻²), the values for S₁ andT₁ are 0.9239 and 0.6795 respectively, and f_(eye) is in units of sr⁻¹.

Still referring to FIG. 11 , a light source at an angle θ with an eye'sviewing direction may produce an illuminance of E₁ (lux or lm·m⁻²) atthe front of the eye, causing a light veil on the retina with luminanceL_(v) (cd·m⁻²) that reduces a contrast of a retinal image. This“equivalent veiling luminance” may represent dazzle. These twoquantities may be related to a scatter function by the followingequation:

$f_{eye} = \frac{L_{v}}{E_{l}}$

To find the equivalent veiling luminance caused by a laser source mayrequire knowledge of an appropriate scatter function together with laserillumination in units of lux. Lasers may be characterized by their powerin watts or their irradiance in W·m⁻², which may be converted toilluminance by use of the following equation:

E _(t)=683V _(λ) U

where V_(λ) is the eye's photopic efficiency at the laser wavelength, λ,and U is the laser irradiance at the observer in W·m⁻². The factor of683 is the lumens per watt at 555 nm for photopic vision—the wavelengthat which V_(λ)=1. The resulting units of E_(I) are lm·m⁻²=lux. Thus, anequivalent veiling luminance, L_(v)(cd·m⁻²), caused by a laser sourcemay be given by

L _(v) =f _(eye)683V _(λ) U

In case of positive contrast (i.e., the target luminance, L_(t), isgreater than the background luminance, L_(b)), the contrast without alaser present may be represented by standard Weber contrast as follows:

$C_{orig} = \frac{L_{t} - L_{b}}{L_{b}}$

For a situation where a laser is present, both target and the backgroundluminance may be increased by the equivalent veiling luminance of thelaser source, L_(v), and the resulting contrast within the eye, C_(v),may become

$C_{v} = {\frac{L_{t} - L_{b}}{L_{b} + L_{v}} = \frac{L_{b}C_{orig}}{L_{b} + L_{v}}}$

With continued reference to FIG. 11 , an eye's threshold contrast fordetection of a target may on background luminance, L_(b), and an angularsize, α (deg), of the target. A contrast threshold C_(thr), detectionmay be characterized according to the following equation:

C_(thr)(L_(b), α, A) = ΩAF${\Omega\left( {L_{b},\alpha} \right)} = \frac{2.6\left( {\frac{\phi\left( L_{b} \right)}{60\alpha} + {L\left( L_{b} \right)}^{2}} \right)}{L_{b}}$${{AF}(A)} = \left\{ \begin{matrix}{{\frac{\left( {A - 19} \right)^{2}}{2160} + 0.99},} & {{{for}23} < A < 64} \\{{\frac{\left( {A - 56.6} \right)^{2}}{116.3} + 1.43},} & {{{for}64} < A < 75}\end{matrix} \right.$

where AF is an age adjustment factor to account for the decrease incontrast threshold with age, A (years), and Ω contains the factors ϕ andL, which depend on the background luminance as given by L=0.054946L_(b)^(0.466); where L_(b)≥0.6 cd·m⁻² and ϕ=log(4.1925L_(b)^(0.1556))+0.1684L_(b) ^(0.5867) and by L=10^(−0.891+0.5275 log L) ^(b)^(+0.0227(log L) ^(b) ⁾ ² where L_(b)≤0.00418 andϕ=10^(0.028+0.173 log L) ^(b) , and by L=10^(−1.256+0.3372 log L) ^(b)where 0.00418<L_(b)<0.6 and ϕ=10^(−0.072+0.3372 log L) ^(b)^(+0.0866(log L) ^(b) ⁾ ² . Ω and AF may include a range of storedand/or experimentally derived values.

In order for laser dazzle to obscure a target, an equivalent veilingluminance of the laser source may be sufficient to reduce a contrast ofthe target to just below its detection threshold contrast. In anembodiment, this may occur where L_(v) causes a contrast, C_(v), to beequal to C_(thr), as follows:

C_(v) = C_(thr)and$\frac{L_{b}C_{orig}}{L_{b} + L_{v}} = {\left. {\Omega{AF}}\Rightarrow L_{v} \right. = {\frac{L_{b}C_{orig}}{\Omega{AF}} - L_{b}}}$

In an embodiment, and continuing to refer to FIG. 11 , MDE is defined asa threshold laser irradiance at an eye below which a given target can bedetected; or equivalently as a measure of a minimum laser irradiancerequired to obscure a given target. This supplements MPE, whichdetermines the safe level of laser irradiance below which there is norisk of permanent eye damage. MDE may be applicable for continuous wavelaser sources and may also be calculated for the average power ofrepetitively pulsed laser sources. MDE in Watts per square meter for agiven target may be derived b equating L_(v) and rearranging equationsdescribed above as follows:

${f_{eye}683V_{\lambda}U} = {\left. {\frac{L_{b}C_{orig}}{\Omega{AF}} - L_{b}}\Rightarrow{MDE} \right. = {U_{threshold} = \left( \frac{\frac{L_{b}C_{orig}}{\Omega{AF}} - L_{b}}{f_{eye}683V_{\lambda}} \right)}}$

Still referring to FIG. 11 , when determining minimum necessarycountermeasures, apparatus may use MDE, and other factors such asambient light, shadows, sun position, other illumination systems,atmospheric conditions such as fog, rain, snow, or the like to calculatein situ, countermeasure effects based on environment.

With continued reference to FIG. 11 , apparatus may pick off and/orsample a small fraction of directed light or other deterrent output, forinstance by using a splitter to redirect a small fraction of outputlight to a sensor and/or photodetector. This and/or other methods ofmeasuring output, as described in this disclosure, may be used byapparatus to keep track per person and/or subject of how much dosage oflight the subject has received; output may be combined with variousother variables to determine energy density, dosage, and effect at thetarget, and several confirmatory systems to keep track of the person'sdosage in regard to laser, audio, RF, or any other energy emitted. In anembodiment, apparatus may first determine the target to engage; this maybe performed in any manner described in this disclosure. Apparatus 100may identify a target and/or subject using any method described in thisdisclosure; identification may be used to determine whether subject is aknown, previous target, for instance according to any internalidentification used to distinguish one subject from another. If thesubject is determined to be a never before seen subject, the apparatusmay start a new safety accumulator, while if the subject is recognizedas having previously encountered the apparatus, the apparatus may usetheir existing known dosage values. The apparatus may store these valuesalong with other critical measurements, such as video, images,environmental conditions, audio, and/or other supporting data, in asecure method, as to retain their values, integrity, and prove a chainof custody for later analysis, such as for forensic examination of thedelivering of countermeasures and safety considerations.

Still referring to FIG. 11 , apparatus may be configured to determineopportune time during which a subject looking at apparatus 100 using anyor all methods therefore described herein and/or use a detected currentpose and/or position of subject to determine an appropriate one or morecountermeasures combined together or in a sequence to use for maximumefficacy.

Further referring to FIG. 11 , apparatus may determine a best “payload”to use on a given subject and provide one dose and/or unit of suchpayload. A “payload” can consist of one or more countermeasures that areto be delivered in a set and/or calculated sequence. These payloads arechosen based on the mode the apparatus is in to illicit a response fromthe subject. For example, a payload may consist of a 638 nm red laserthat is on for 50 μs, off for 50 μs, on for 100 μs, then off for 110 μs,in addition to a 520 nm green laser that has a repetition rate of 12hertz, with a 25% duty cycle, and a single, loud impulse sound fileplayed 2 seconds into the initiation of the payload. The apparatus stilltracks each countermeasure for safety levels individually. For instanceand without limitation, apparatus may compare measured and/or determinedoutput power with other possible system settings, such as galvanometerand/or other beam-steering apparatus's positions, beam divergence, speedof a laser sweep, speaker setup, or the like. Apparatus may use realtime measurement of energy to calculate a temporal accumulated dosage.For example, if apparatus is delivering 1 mW/cm² of 520 nm laser for 2ms, apparatus may add to a subject's accumulator the specific laserenergy delivered over the amount of time including wavelength, or thelike, so a normalized MPE can be calculated using mW/s/cm² or similaracceptable method.

Still referring to FIG. 11 , apparatus may use one or more factors tomodify a calculated MPE for a subject. Such factors may include, withoutlimitation, whether subject is using glasses or other eyewear toattenuate laser or other directed light, an angle of a subject's gazewith regard to the laser, calculation of MPE separately for differentregions of the retina, for instance by splitting the retina conceptuallyinto two or more areas and calculating a different dosage per angleentry into the eye, what atmospheric conditions may have attenuated thelaser, or the like. This would also extend to other countermeasures,such as, but not limited to, audio, RF, and kinetic countermeasures, andtheir applicable approved safety dosage limits.

In an embodiment, if the apparatus 100 can determine the subject'sidentity, whether through distinguishable permanent characteristics ortheir true identity, this metadata can be used to track the subject'sidentity across separate encounters, including, but not limited to, thedosage over longer periods of time, including use of this info for otherreasons. In an embodiment, if subject's accumulator for MPE or otherexposure is exceeded, apparatus may withhold engaging the subject,possibly limit to only observing and analyzing what the situation is,and possibly raise the alarm state of the system or other appropriateaction. A subject's dosage may be tracked until the necessary timeperiod expires, such as a recovery time of 24 hours or other amountsbefore MPE “resets” according to applicable standards. Thiscountermeasure usage data may be stored for analysis and used to improveand/or expand the capabilities of the system.

Still referring to FIG. 11 , there may be a certain amount of energy perpulse generated by a light deterrent such as directed light deterrent,where amount of energy may be a fraction of total MPE. In an embodiment,each pulse may be approximately 200 ms in duration, for instance tomatch a period of exposure prior to blinking and/or turning of headand/or eyes, which may take approximately 150-200 ms. Pulses mayalternatively be less, such as 100 ms, 20 ms, or the like. A counter inapparatus 100 may track a number of pulses of light emitted; there maybe a counter per person that drops a number of “photonic bullets,” whichmay be distinct pulses, in a “magazine” representing MPE; for instance,if MPE is 10 seconds of light reception at an emitted transmissionlevel, 100 pulses of 100 ms may be in the “magazine,” after which for arecognized person the apparatus may not emit light deterrent at thatperson, who may be identified using any means and/or method foridentification described in this disclosure. MPE may be calculated basedon dwell time. As used in this disclosure, “dwell time” is an amount oftime a person actually has a laser and/or other light source on the eyeof the person. In an embodiment, beam spread may be selectedsufficiently to make sure that a spot on a subject is big enough tonullify aversive response such as a turn of head and/or eyes during apulse. In some embodiments, determination and/or calculation of MPE maydepend on a plurality of different factors. For instance, MPE may becalculated in some embodiments as a function of distance to a target,dwell time, divergence of a beam, and/or wavelength of light.Calculation may be determined using any suitable mathematicalcombination of such factors, such as weighted sum, multiplicationtogether, or the like.

Still referring to FIG. 11 , determination of MPE and/or other safetymeasures may depend on distance, radiance, beam divergence, whether itis light or dark outside and/or retroreflection to determine whether asubject is intoxicated or otherwise has some degree of pupil dilationand/or contraction outside a normal range for a degree of ambient light.MPE may be determined according to any standard, including withoutlimitation ANSI 136.4. Alternatively, a standard may include ED50,representing a dose having a 50% probability of producing a criterionresponse. ED50 may not constitute a threshold; it may not be possible todetermine a true threshold for these effects. An ED50 for laser-inducedretinal injury may be dependent on a number of factors. Inherent to alaser and/or other directed light deterrent source may includewavelength, pulse duration, and pulse repetition rate. In an embodiment,damage to retina may depend on melanin absorption in a retinal pigmentepithelium (RPE) determinations of melanin absorption may be reasonablyapproximated by the function

Aλ=1−e ^(−α) ^(λ) ^(s)

where α_(λ)=α₀(λ₀/λ)^(3.5). An absorption length, s, may be 5 μm. A fitto an RPE absorption data may be obtained when α₀ is set to 4100 cm−1 atthe wavelength λ0 of 380 nm. Energy absorbed by the RPE Qr_(λ), may begiven by

Qr _(λ) =Qp _(λ) T _(λ) Tb _(λ) A _(λ)

where Qp_(λ) is the energy at the cornea within the area of the pupil,T_(λ) the transmission of the preretinal ocular media at wavelength λ,Tb_(λ) is the transmission of blood assuming a 5-μm absorption path, andA_(λ) is the absorption of the retina at wavelength λ, as given by

A _(λ) =A _(λ)(RPE)+A _(λ)(H ₂ O)

Thus, a relative retinal hazard as a function of wavelength ofcollimated laser energy incident at the cornea may be given by

$\frac{Qp_{\lambda}}{Qr_{\lambda}} = \frac{1}{\left( {T_{\lambda}Tb_{\lambda}A_{\lambda}} \right)}$

Qrλ may alternatively or additionally e expressed by the function

${Qr_{\lambda}} = {Q{r_{0}\left( \frac{D_{\lambda}}{D_{0}} \right)}^{X}}$

where Qr0 is the required energy for a minimum retinal irradiancediameter D0, and Dλ the chromatic aberration-induced diameter atwavelength λ. Thus,

${Qp_{\lambda}} = {Qr_{0}\frac{\left( {D_{\lambda}^{X}/D_{0}} \right)}{T_{\lambda}Tb_{\lambda}A_{\lambda}}}$

The value of X, which determines the variation of ED50 with the retinalirradiance diameter, varies from a value of 2 for exposures ofnanoseconds to microseconds to a value of 1 for 0.25-second duration andlonger exposures. As a result, a time relationship predicted by thermalmodels may closely approximate a dependence shown by the bioeffectsdata. Values in energy of the ED50 for visible laser exposure mayincrease proportional to t^(0.75) for exposure durations longer thanabout 10 microseconds. Safety guidelines may adopt an n−¼ relationshipfor determination of MPE for exposure to repetitive pulsed lasers.Exposure for any single pulse in a pulse may not be able to exceedsingle-pulse MPE multiplied by a multiple pulse correction factor C=n¹⁴, where n is a number of pulses. A relationship governing MPE may beexpressed by

MPE(RP)=MPE(SP)n ^(−1/4)

where MPE(RP) is a maximum permissible exposure for the repetitive pulsetrain expressed as energy per pulse and MPE(SP) maximum permissibleexposure for a single pulse from the same laser. On the average

ED₅₀(RP)=ED₅₀(SP)n ^(−1/4)

This result may be relatively independent of a wavelength, pulseduration, or pulse repetition frequency of a laser. Models based on athermal damage mechanism cannot readily explain this result. A safetymargin, defined to be a ratio ED50/MPE and commonly assumed to 10, maybest be viewed as a measure of confidence in the experimentallydetermined value of ED50. That degree of confidence may be greater forsome combinations of exposure parameters than for others. The leastconfidence is assigned to ED50 for pulsed exposure to a highlycollimated laser beam. A Spot-Size dependence may include uncertaintyregarding a true dependence of ED50 on retinal irradiance diameter fordiameters less than 80-100 μm, which may leave that the threshold fordamage might be lower in the actively blinking, actively accommodatingeye of an alert young human.

Still referring to FIG. 11 , people may move their eyes and/or head uponglare. In an embodiment, where fovea is a target area, apparatus 100 mayuse retroreflection to determine where eye is, and find center of pupilwith that targeting and/or retroreflection laser, and then shootdirected light deterrent immediately and/or microseconds later. This mayoccur so fast that it is safe to assume successful strike on targetarea. This may cover a 10 cm burst of energy, within which a person maybe unable to avoid interdiction. MPE may operate as a “magazine” ofenergy may be transmitted, which may be transmitted a small amount at atime, to permit interdiction and/or deterrence multiple times prior toreaching MPE. Standards may require allowance of recovery time after MPEor a fraction thereof above a threshold level has been reached.Apparatus may determine that recovery time has elapsed, and/or a recordof transmissions at subject, by using any means of recognition and/oridentification of subject, including without limitation location inspace, tracking, facial recognition, and/or other metrics.

In some embodiments, and still referring to FIG. 11 , eye movement andhead movement may reduce actual dwell time on a retinal point. Forinstance, eye movement may cause a given light source to illuminate alarger retinal area with a more diffuse amount of energy, which may belower, per point on the retina, than might be assumed given a degree ofconcentration and/or collimation of a light source. As a furtherexample, head movement may cause illumination of an area such as themacula and/or fovea to cease once subject's head has moved by more thana certain amount, reducing the overall time of exposure to on-axisillumination. Either or both of these motions may reduce the overallenergy delivered in any given period of illumination by directed lightdeterrent, which may permit direct light deterrent to output a greaterquantity of energy while remaining within safety limits. A beam may bedithered along a vertical axis to ensure striking the fovea.

Still referring to FIG. 11 , retroflection may be detected using directand/or indirect retinal reflection. Because of eye fluorescence, theremay be orthogonal reflection pathways, which may cause retroreflectionto occur when a person is looking sideways. Pose detection may beutilized to develop confidence about direction of gaze, by detectingother anatomical features.

Further referring to FIG. 11 , directed light deterrent 156 may beconfigured to respond to gaze detection as feedback. For instance, wherea retinal reflection, such as a “cat's eye” reflection, of light fromdirected light deterrent 156 and/or any other light source is detectedby imaging device 104, processor 136 may determine that subject 308 isexposed for a directed light deterrent 156 action as described above,and/or that subject 308 has been successfully hit by a deterrent action.

Still referring to FIG. 11 , processor 136 and/or remote device 140 maydetect one or more reflective surfaces in subject area during baselineestablishment as described above, where reflectivity may be measuredaccording to intensity of returned and/or backscattered light. In anembodiment, processor 136 and/or remote device 140 may determine anglesof reflection off one or more such reflective surfaces toward visualband of subject 308, permitting deterrent actions to be performed byreflection off reflective surfaces. For example, and without limitation,where subject 308 has turned away from apparatus 100, placed a hand orarm over eyes, or the like, a direct shot from directed light deterrent156 may be difficult or impossible, while a reflected shot may wholly orpartially access visual band. Each of the above-described evasiveactions by a subject 308 may be detected and/or predicted usingmachine-learning and/or anatomical landmark tracking processes asdescribed above.

With continued reference to FIG. 11 , directed light deterrent 156 mayalternatively or additionally be configured to render images usingprocesses analogous to laser painting on walls and/or other structuralfeatures in or around subject area; this may be used to convey verbal,pictorial messages, and/or video messages to subject 308 such asdirectional arrows or the like, which may be used to indicate to subject308 how to vacate subject area and/or otherwise comply with directionsof apparatus 100, a user thereof, or the like. Video messages can take anovel form of videos processed with edge detection to provide a highcontrast image and optimize the video for laser scanning or other beamsteering methods. In addition, directed light deterrent 156 may renderuser interfaces on the wall that when combined with an imaging device104 and processor 136, approved subject 308 may interact with saidrenderings to interact with apparatus, such as, but not limited to,option menus, system settings, system modes, internet functions, and/orother uses of user interfaces to the apparatus 100.

With continued reference to FIG. 11 , directed light deterrent 156 mayalternatively or additionally be configured to flash a pulse of lightfrom the light source 1004 or an alternative light source, such as, butnot limited to, an omnidirectional LED. This may cause a startle reflexin the subject 308 and result in them gazing directly at the apparatus100, exposing their eyes optimally for a possible use of a directedlight deterrent. In addition, the apparatus may use a plurality ofdeterrents in attention getting modes to have the subject 308 gazedirectly at it, such as, but not limited to, a short burst of sound.

With continued reference to FIG. 11 , directed light deterrent 156 mayalternatively or additionally be configured to use motion sensor 132IMU's accelerometer data by processor 136 to determine the apparatus'100 orientation to level in 3 dimensions, including inclination androll. This angular measurement can be used to determine relative anglesto subject 308 and used to enhance calculations, such as, but notlimited to, eye safety and dazzle energy calculations for off angleuses. The IMU data may also be used to calculate directionality of thedirected light deterrent 156 to avoid shining light into the sky thatmay interfere with pilots, satellites, and/or other aerial imaging orvision systems.

Referring now to FIG. 12A, an exemplary embodiment of a multiplexingmirror component 1200 is illustrated. Multiplexing mirror component 1200may include a first mirror 1204 and second mirror 1208; first mirror1204 and second mirror 1208 may have axes of rotation about whichreflective panes of mirrors may rotate, such that an angle of reflectionof light, which may include ToF light, laser light, directed lightdeterrent light, ambient light or any other light emitted from apparatus100 and/or accepted into directed mirror deterrent may be able to covera range across one dimension of a field of vision. Axes of rotation maybe orthogonal or otherwise span two dimensions permitting coverage of afield of vision, where “coverage” as used here means ability to emitlight reflected off of the mirrors to any point within the field ofvision and/or receive light from any such point and direct such lighttoward sensors positioned to receive light reflected off first mirror1204. For instance, and without limitation, first mirror 1204 may be anx mirror, and second mirror 1208 a y mirror of an xy galvanometer orsimilar device. A first emitter 1212, which may include any emitter ofelectromagnetic radiation described in this disclosure, may bepositioned such that light emitted therefrom is reflected off of firstmirror 1204 onto second mirror 1208, and thence out an aperture and/orinto a field of coverage, such as subject area, when first mirror 1204is in a first position. A second emitter 1216 may be reflected away fromsecond mirror 1208 when first mirror 1204 is in first position. In anembodiment, placement of a camera, rangefinder, infrared emitter and/ordetector, or the like behind first mirror 1204 may have an ability totrack objects within a field of coverage of multiplexing mirrorcomponent 1200; in some embodiments, use of multiplexing mirrorcomponent 1200 to perform such scans may permit very rapid sensor scansof subject area. Multiplexing mirror component 1200 may be used totarget and emit directed light deterrents, ToF or other light radardevices such as LIDAR rangefinders, or any other emission sources thatmay occur to a person skilled in the art upon viewing the entirety ofthis disclosure.

Referring now to FIG. 12B, an exemplary embodiment of multiplexingmirror component 1200 with first mirror 1204 in a second position isillustrated. In this configuration, first emitter 1212 no longer isreflected to second mirror 1208 while second emitter 1216 is. A lightsensor in position of second emitter 1216 may similarly be used to scansubject area instead of a light sensor in position of first emitter1212. In some embodiments, first mirror 1204 may have multiple positionscorresponding to multiple emitters having different positions such thatselective repositioning of first mirror may result in selectivereflection of each emitter's light, allowing rapid switching betweenemitted light sources, for instance to vary incident colors at asubject's face, to alternate visible light with lens-fluorescing light,or the like.

Further referring to FIG. 12B, first mirror 1204 and/or second mirror1208 may be implemented using any kind of reflector, including withoutlimitation reflectors having different properties for differentwavelengths. For instance, in an embodiment where there are multiplepositions for first mirror 1204 first mirror 1204 and/or second mirror1208 may include a reflector, such as a reflective grating, withdifferent angles of reflection for different wavelengths positions offirst mirror 1204, second mirror 1208, and/or an emission source may beselected such that a reflection angle at a wavelength of an emittedlight source will cause the light to center on second mirror 1208 and/orotherwise correct for offset positions such that a central position offirst mirror 1204 and/or second mirror 1208 corresponds to a centralposition of a desired field of coverage, to maximize a possible field ofcoverage.

In an embodiment, and still referring to FIG. 12B, first mirror 1204and/or second mirror 1208 may be positioned and/or designed to spread abeam vertically to catch eyes of persons who are looking downward and/orupward; this may be combined with a mount that permits directed lightdeterrent to be moved up or down vertically, for instance using anelevator screw, “worm gear,” pulley system, and/or pneumatic, hydraulic,or other actuators, permitting light to be directed at various angles,from various heights. A choice of height and/or angle may be determinedusing retroreflection of light radar and/or any other imaging techniquesand/or devices described in this disclosure.

Referring now to FIG. 13 , a graph illustrating exemplary outputpatterns, represented as on-off duty cycles, of different wavelengths,which may be emitted using any devices, reflectors, or the like as partof directed light deterrent. A first wavelength may be emitted on afirst duty cycle, which may vary according to any instructions generatedby apparatus 100. A second wavelength may be emitted on a second dutycycle. A third wavelength may be emitted on a third duty cycle. In anembodiment, alternation, combination, and/or on/off cycles of differentwavelengths may be combined for a desired psychological and/orphysiological effect at subject, as described in further detail in thisdisclosure.

In an embodiment, and still referring to FIG. 13 , apparatus 100 mayinclude two systems of laser or otherwise directed light: a first lightradar system at a first wavelength set and a second directed lightdeterrent system at a second wavelength set; first wavelength set, andsecond wavelength set may be disjoint. For instance, first wavelengthset may include one or more wavelengths useful for light radar, such aswavelengths in the far red to near infrared spectrum, or approximately750 nm to 980 nm. First wavelength set may include, for instance, awavelength of approximately 850 nm, a wavelength of approximately 805nm, or the like. Light radar may operate at near invisible wavelengthsand/or slightly visible wavelengths. Light radar may transmit at 5nanosecond bursts, which may be of overly short duration to be visible.As a result, light radar may operate without reaction and/or awarenessof subject. Accurate light radar may prevent target acquisition jitterfor directed light deterrent. Second wavelength set may include visiblespectrum wavelengths, such as red, blue, green, and/or violet light asdescribed above. First wavelength set may be used for detection ofretroreflection of retinas or the like, permitting operation of asingle, unified targeting system.

Referring again to FIG. 1 , directed light deterrent 156 may include amicrowave or MMW source 160. Microwave or MMW source 160 may emit lightand/or radiation at a wavelength that causes non-lethal pain, burningsensations, and/or other intended effect; for instance, and withoutlimitation, microwave and MMW source 160 may generate radiation having afrequency of approximately 96 GHz that interacts with a subject's painreceptors 1/64″ under the skin surface, causing a perceived intense painof burning, without any tissue damage actually occurring as part of aneurostimulation deterrent.

Still referring to FIG. 1 , deterrent component 152 may include adirected sound source 164 and/or directed audio deterrent. Directedsound source 164 may include a sound source that may be aimed atspecific subjects 308 in a manner analogous to a directed light source.Directed sound source 164 may include, without limitation, a long-rangeacoustic device (LRAD), which may use a plurality of closely packed,coordinated piezoelectric transducers to produce highly directionalsound waves. A laser generating localized plasmas in the atmosphere maybe used to create modulating plasmas near the subject 308 such thataudible sound is produced for warning messages and/or other sounds todeter the subject. An ultrasonic carrier wave may be modulated to createaudible sound, including through parametrically generated pressurewaves, which may be selectively audible to a person who is standing inthe direction of transmission while being far less audible oressentially inaudible to a person who is not. A spark gap emitter may bemodulated to create audible sound, including through sending electronsacross a spark gap of a plasma arc, wherein the transmission results inan emission of a high frequency wave. In an embodiment, very highstartup speed devices, like spark gap devices, may evince a strongerstartle response from subject. In an embodiment, and without limitation,spark gap emitter may emit a 165-175 dB wave. In an embodiment, a sparkgap emitter may function to create “artificial lightning” at a setfrequencies for extremely loud audio output. In an embodiment, spark gapaudio is wideband, and thus may be used as-is for deterrence,obfuscation of other audio such as a person on a megaphone or the like,or may be manipulated using mechanical or spatial filters, pulse widthmodulation, pulse coded modulation, amplitude modulation, or the like tocreate more defined sounds. In some embodiments, where multiple gapgenerators are placed in an array, such as without limitation a linear,2D, or 3D array in repeating, fractal, or other deliberate pattern,standard beamforming methods in conjunction shaping of cavity(ies) toenhance the spark gap acoustics may be used to create an electronicallysteered and/or tuned acoustic source from the spark gap generator. Insome embodiments, a spark gap emitter may be used to acoustically map aroom, area, outdoor environment, or the like. Spark gap emitter may beso employed so the system may best apply acoustic countermeasures,possibly taking advantage of occlusions, objects, materials, or the liketo bounce, aim, avoid, or the like when steering around acousticalcountermeasures. Apparatus may even use such methods to determineranging, weather, or other phenomenon. In an embodiment, the apparatusmay use one or more methods to reduce the electrical potential of theair to be traversed by a spark; for instance, the apparatus may ionizethe air using electrical sources or use ultra-short pulse lasers tofilament the air. In some embodiments, modifying the potential and/orresistivity of air permits more volume control and/or lowers voltagethreshold to generate sparks, which may reduce energy consumption.

Continuing to refer to FIG. 1 , methods to generate plasmas at adistance may be used to generate sound at a given location. This couldinclude use of nano second and/or femto second laser combining togenerate necessary energy densities at a given distance to createlocalized plasmas. These plasmas may then be modulated to create soundfrom their acoustic shockwaves. This may include modulation of a singlespot to create audible, broadband audio, or may be more complex, withmultiple modulated sources in a 3D arrangement relative to themselvesand/or to the target to generate complex sound fields using high orderambisonics or other beam forming techniques. Laser energy at highintensity but very short duration may generate a small plasma ball inair or other gas, with cavitation and/or pressure waves generated byrapid expansion and contraction thereof producing sound waves havingfrequencies and/or other characteristics driven by frequencies and/orcharacteristics of lasers used to generate the plasma balls.

With further reference to FIG. 1 , multiple LRAD speakers, and/orsequential aiming of a single LRAD speaker may be used to send differentaudio signals to different subjects 308. This may be used to generatehighly specific instructions such as directions to leave subject areawhich vary based on a position of a receiving person, and/or to generatedistinct messages and/or sounds to distinct persons to cause furtherconfusion and/or disorientation. For instance, a first subject 308alerted by this means may hear a police siren, while a second subject308 may hear a barking dog, a third a voice saying “hey,” or the like.

With further reference to FIG. 1 , LRAD or other directed sound sources164 may be mounted on two or more rotational devices, permittingscanning and aiming across a portion or all of subject area. Variedaudio counter measures may use multiple sounds to drive a single subject308 into a state of anxiety, confusion, or concern. All of these stimulimay be varied by location, behavior, velocity. Additional or alternativeacoustic sources may be employed, such as an ultrasonic transducergenerating 18,000 Hz sound to preferentially deter teenaged and/or youngadult persons from subject area. Additionally or alternatively, directedsound source 164 may include one or more audio signals that appear tofollow and/or track an individual. For example, and without limitation,an audio signal directed towards a first location may occur, wherein assubject 308 moves around the subject area, directed sound source 164 maymove and/or shift the direction of the audio signal to track and/orfollow subject 308. In an embodiment, where an audio transducer has afield of sound such as a 60-degree field of sound, sounds may be aimedwithin that field to capture particular subject of a plurality ofsubject. In an embodiment, where apparatus 100 has located eyes of asubject, or other landmark feature, apparatus may know a location ofears, and how to cause sound to appear to come from one location oranother, depending on a desired effect at subject.

Still referring to FIG. 1 , audio deterrent may include a system thatuses some number, such as 25, horn-shaped items with compression driversto direct sound in a particular direction. Electronic beam forming maybe used to aim sound; it may be possible for aiming to be varied alonghorizontal and vertical axis up to 60 degrees. In an embodiment, soundproduced may be up to 140 Db at 30 meters away in a highly directional,extremely focused beam. Sound may be producible So behind each person insubject area, sending each, concurrently, a different audio message. Asubject may hear, 30 meters to their left a barking dog, and 10 feet inanother direction, a police siren. Then a voice in front of subject mayidentify them, referring to a distinguishing feature thereof, and mayissue commands. In an embodiment, this may have the effect of convincingsubject that a live human is watching their every move, following themwith their voice. Hearing a natural, authoritative voice speaking tothem, calling them out by what they are wearing and a direction they arewalking, an illusion may be established and maintained, increasing alikelihood of de-escalation. This may function at least to delay needfor human intervention, and thus may act as a force multiplier for humanbeings. In an embodiment, human behavior may altered by sound;alterations may be characterized as innate and/or genetic, adapted basedon evolutionary experience, and/or culturally informed. Psychoacousticsystems may be naturally adept at listening to human speech. Persons mayfurthermore be especially attuned to speech that they deem is directedat us (both spatially and contextually) as an individual. In anembodiment, apparatus 100 may incorporate natural human speech, capableof saying anything on demand, with parametrically controllableexpressivity and emotion. Instructions to TTS models may be provided inSSML, which is a self-describing XML-style standard that allows text tobe tagged for a TTS engine and rendered according to desired tonalinflection, emotional intent, pitch, cadence, or the like. An exemplarySSML example may include:

-   -   <prosody rate=“slow” pitch=“−2st”>Can you hear me        now?</prosody>Another example may include:    -   <prosody rate=“slow” emotion=“maxAnger” alertLevel=“9”        pitchCurve=23.3>Hey you in the blue shirt. Leave the        premises!/prosody>

In an embodiment, neural TTS models may be implemented on embeddedprocessors, without any network connectivity. This may have lowerlatency than cloud communication at a similar level of quality. In anembodiment, system and/or apparatus may use above-describedfunctionality to incorporate natural human speech, capable of sayinganything on demand, with parametrically controllable expressivity andemotion. Parameters may be provided and/or adjusted to correspond to oneor more “expressive components,” including speed of speech, volume,pitch, and/or vocabulary—for instance, expressive components for ashocking or strong audio output may include strong or forceful language,a rise in intonation, and increased volume. These parameters may bedetermined by using a behavioral classifier to determine what a subjectis doing initially and/or in reaction to stimuli. Behavior and/orprogression of behavior as classified may be linked by anotherclassifier and/or a lookup table to a tone of voice, prosody, wordchoice, expression of response, or the like.

Referring now to FIG. 14 , an exemplary embodiment of a steerablespeaker system 1400 is illustrated. Steerable speaker 1400 may becapable of being swept across a large field and/or to transverse a verytall wall interdicting a single or multiple intruders. Steerable speaker14 may be fully controllable for coverage of 30 degrees in both thehorizontal and vertical plane while producing a plurality such aswithout limitation 8 individual simultaneous sound fields, each with itsown unique acoustic payload which may include sounds, voice output,and/or words as described elsewhere in this disclosure, as well as SPLvolume level controlled under the direction of other systems withinand/or connected to apparatus 100. Steerable speaker 1400 may alsoproduce extraordinarily levels of “white noise” or other noise, suchthat anyone in its field will experience complete disintermediation ofcommunications—the inability to hear another's voices.

Still referring to FIG. 14 , steerable speaker 1400 may include one ormore horns 1404, such as without limitation a plurality of horns in amatrix array, such as a 5×5 matrix horn array. Dimensions andsource-to-source distances may be imposed by a maximum SPL that to beachieved. Such arrays may achieve very good steering precision into avocal intelligibility range (2 kHz to 3.2 kHz). In embodiments,significant beam tightness of a projected audio beam may be achieved.For instance, at 57 m distance, beam tightness may range form 60 degreesat 500 Hz to approximately 3 degrees at 7 kHz, with approximately 30degrees at 2 kHz.

Still referring to FIG. 14 , steerable speaker 1400 may create apowerful sound beam out of a compact loudspeaker array. In anembodiment, each horn element 1404 may be optimized to provide properacoustic loading in a desired range of use which is between 450 Hz up toabout 7.5 kHz. A compression driver of each horn 1404 may include, in anon-limiting example, a titanium dome diaphragm installed in front of aphase plug, which may have a moderate compression ratio. This may allowa transducer incorporated therein to reach high sound pressure levels,still keeping the distortion level acceptable. Compression driverdiaphragm may feature a high compliance suspension. This aspect mayadvantageously assure a driver's ability to present its naturalmechanical resonance frequency significantly below the range of use,thus assuring safe operation and good quality of voice or other tonalitywithin an intended range of use.

Still referring to FIG. 14 , steerable speaker 1400 may be arranged bycreating an array of multiple horn cells 1404, each one loaded with oneor more high output compression drivers. In the case of multiple driversloading, a high efficiency and accurate manifolding system may beprovided in order to ensure efficient loading and accurate phasesummation at the highest frequency to avoid detrimental interferences atthe top of the bandwidth. Array of multiple units may be arranged in theform of a “n x m” matrix to provide steerability and/or directivitycontrol, both in horizontal and in the vertical planes. For instance,and without limitation, an array may be arranged in the form of asquared matrix of horn elements 1404, such as a 5 by 5 element matrix.An odd number of elements may preserve a better stability of a centerlobe in terms of polar response than an even number of elements, evenwhen side lobes can become quite severe.

Continuing to refer to FIG. 14 , a specific choice of horn size andnumber of units 1404 belonging to a target SPL that has been assigned tothe system; target SPL may include, without limitation, a 140 dB SPLpeak at 30 m distance from the source. A size of horn elements may beselected as a tradeoff between a maximum SPL that can be generated andthe creation of side lobes above a certain frequency. One or more designfeatures may be optimized with different parameters to reduce the amountof side lobes and their intensity, for instance and without limitationby using smaller horns 1404 at the expenses of maximum SPL and/orchannel count, or it may be optimized to further increase the SPLincreasing the horn size and the number of elements at the expenses ofside lobe generation and overall dimensions.

Still referring to FIG. 14 , an algorithm used for steering processingmay mix standard beam steering techniques based on the application ofindividual delay and level variation to each single cell to determinethe direction of the main beam, with steering functions determined byassignment of target response functions in a covered space andperforming direct inversion of a filter matrix to optimize plantfunctions. This mixing technique may include combining an SPL yield ofsimple beam steering based on a delay matrix with an advantage ofadditional refinements carried on by a matrix of functions that mayreduce aggressivity of side lobes in most cases.

With continued reference to FIG. 14 , in a loudspeaker system 1400described above, each element 1404 may have its response optimized forcorrect summation and to generate a homogeneous response when an arrayis pointed straight and not steered around. Moreover, dedicatedprocessing channels may also take care of individual workloads ofloudspeaker elements in order to protect them from diaphragm or voicecoil damage due to excessive drive unit power. Input voltage and/orinput current that are sent to each driver unit may be monitored andsubject to a power limiter to not overcome the power handling of eachloudspeaker in the long term. Voltage and current sent to a loudspeakermay also be used, in conjunction to a prediction software, to make surethat at given point in space where the loudspeaker beam is pointed, amaximum SPL delivery to a single individual will never overcome a limit,such as a 140 dB limit, above which hearing impairment could beproduced.

Still referring to FIG. 14 , beam steering parameters may be calculatedin real time by a dedicated implementation of a previously explainedalgorithm, and a current direction of a main beam may be assigned by anexternal control. A camera and/or other imaging device as describedabove, which may be dedicated to steerable array 1400 and/or may includeany imaging device of apparatus 100, may be able to recognize presenceof a person in an area of interest, and may be capable of determining aposition in space of such person and/or subject and a distancetherefrom. These data may be sent in a continuous real time stream tobeam steering algorithm, updating in real time a position in space wherea main beam may be sent. A dynamic real time implementation of beamsteering algorithm may be realized either or both with real timecalculation and using a look up table or similar data structure toselect an appropriate function among a high number of precalculatedtarget functions.

Further referring to FIG. 14 , messages and sounds to be reproducedand/or sent in each desired direction may be saved into a sound bank,which may be implemented using any suitable form of digital memory.Alternatively or additionally, sounds may be provided from a livesignal. Beam steering algorithm may be designed to steer multiplesources in different directions, simultaneously, since system may beconfigured to manage multiple input signals, where each input mayrequire the entire beam steering mechanism to be instantiated.

In embodiments, and continuing to refer to FIG. 14 , output capabilitymay accomplish a maximum output capability of 140 dB SPL peak (128 dBSPLRms) or more (full bandwidth) at 30 m distance (98 ft) when 3 beams areshot in −20, 0 and +20 degrees, simultaneously or quasi simultaneously.This output capability may be rated for short period of time <=10Seconds. Output frequency response may range from approximately 450 Hzto approximately 7.5 kHz when measured at 1 m, with deviations from flatto be held to within +/−6 dB, specifications listed below. System 1400may be able to be steered both horizontally and vertically. Steeringcapability may reach +/−30 deg on both planes for the vocal rangef=<3000 Hz. Fully steerable bandwidth may expand increasingly a 3000 Hzlimit when steering angle is less than 30 degrees. Steering capabilitymay include a capability to manage multiple beams (up to 3) in differentdirections and with different sound sources. System may be able tosustain the maximum power handling continuously for at least 120seconds, followed by a maximum off period of 7.5 seconds, for a totalevent duration of 15 minutes, without suffering thermal or electricalload problems. Available maximum output capability may be reducedaccordingly as per occurring thermal and dynamic power compression.Protection limiters may also be set in order to reduce diaphragm fatigueand potential breakout.

In an embodiment, and still referring to FIG. 14 , system 1400 may beable to play multiple audio stimuli concurrently in “cones” of soundopening from a port to a few meters, which can aim to differentlocations in a subject area and/or space. A plurality of such conesand/or beams of sound may be projected simultaneously. For instance, ifthere are 8 users system 1400 may be able to create a beam focused oneach of them. Apparatus may be able to select the most aggressivesubjects and send them tailored messages referring to identifyingfeatures. Beams may be spaced apart sufficiently such that a beam on asubject drowns out a beam that is not on the subject: if one source is10 dB louder than another subject will be unable to hear the othersound. As a result, subject may only hear the sound pointed at thatsubject. Thus, apparatus may be able to present distinct stimuli to eachperson, or different sounds at different spots for the same person. Insome embodiments, people may react most aggressively in terms of fearand anxiety if they are not sure where the sound is coming from. System1400 may have the ability to deliver specific information and/orpayloads we can deliver with a TTS engine and or create multiplechannels placed in different locations to create “holographic” acousticimagery, for instance placing different audio stimuli at differentlocations relative to a subject as described in further detail herein.

With continued reference to FIG. 1 , deterrent component 152 may includean electric shock or Human Electro-Muscular Incapacitation (HEMI) device168. Electric shock or HEMI device 168 may include a “stun gun” and/ortaser, which may, for instance be able to fire two or more cartridges upto 25 feet, as in a taser area denial system (TADS). Alternatively oradditionally, shock device may be used to electrify one or more objectsor surfaces to generate a shock upon contact or near approach by a user.Alternatively or additionally, shock and HEMI device may use generatedplasmas, electric fields, ionization, and/or other methods to convey aneurostimulation to the subject from a distance.

With further reference to FIG. 1 , deterrent component 152 may includeone or more sources of noxious smells and/or other chemical deterrents172, such as pepper spray, “skunk” or malodorant weapons, tear gas,pacifying agent, pepper balls, chemical darts, or the like, which may besquirted out of nozzles in liquid, vapor, and/or gaseous form, or firedas projectiles that break open and/or otherwise disperse such irritants.Chemical deterrents may further take a form of a sticky and/or slipperysubstance released onto a walking surface to make proceeding and/oraggressive motions more difficult. Further deterrents may include paintballs, pepper balls, air bursts, electronic darts, chemical darts, orthe like.

With further reference to FIG. 1 , deterrent component 152 may includeone or more sources of entanglement device 176, such as, but not limitedto, nets, bolas, and/or other entanglement or entrapment devices thatare launched ballistically at the subject 207 in order to limit or stopthe subject's 308 ability to move normally. The deterrent component 152may use the processor 136 and imaging devices 104 to calculate andpredict, based on the distance and movement of the subject, in additionto the ballistic properties of the entanglement device, a correctedpoint of aim to launch the entanglement device.

With further reference to FIG. 1 , deterrent component 152 may includeone or more sources of obscurant delivery system 180, such as, but notlimited to, methods to obscure the vision or other senses of the subject308. For instance, and without limitation, these may includebiodegradable fog generators, smoke generators, and/or water mistgenerators. The effect can be further enhanced by illuminating theobscurants with light sources from the deterrent component 152.

With further reference to FIG. 1 , deterrent component 152 may includeone or more sources of blunt force or kinetics delivery devices 184,such as, but not limited to, bean bag round launchers, stingballs,non-lethal grenade or payload launchers, water cannons, air cannons,and/or other methods to deliver non-lethal kinetic effects to thesubject 308.

With further reference to FIG. 1 , deterrent component 152 may includeone or more sources of marking delivery device 188, such as, but notlimited to, paintball launchers, dye sprayers, paint sprayers,malodorant launchers, and/or other methods that will optically, odorant,or other senses tag a subject 308 for later identification. This caninclude dyes that are visible or non-visible to the human eye thatrequire special lighting or other methods to detect at a future time. Inaddition, the dyes, paints, and/or markers may be formulated to makeremoval extremely difficult. Digital “marking” using light and/oraugmented reality output may alternatively or additionally be used.

With further referring to FIG. 1 , deterrent component 152 may includeone or more sources of electromagnetism and/or electromagneticdeterrents. As used in this disclosure an “electromagnetic deterrent” isa deterrent that emits a high-powered electromagnetic wave. For example,and without limitation, electromagnetic deterrent may emit a 95 GHz wavetowards a subject. As a further non-limiting example, electromagneticdeterrent may emit a wave comprising a wavelength of 3.2 mm. As afurther non-limiting example, electromagnetic deterrent may include oneor more sources and/or deterrents that penetrate the top layers of theepidermis of a subject, wherein the penetration is absorbed within 0.4mm of interacting with the epidermis. In an embodiment, and withoutlimitation, electromagnetic deterrent may be oriented along a turretand/or actuator, wherein electromagnetic deterrent may be rotated and/orshifted as a function of the subject. In an embodiment, and withoutlimitation, electromagnetic deterrent may be rotated and/or shifted as afunction of localizing the target to a subject's physiological tissuesthat are susceptible to the electromagnetism such as, but not limitedto, nose, lips, eyes, ears, and the like thereof. In an embodiment, andwithout limitation, electromagnetic deterrent may be rotated and/orshifted to interact with a plurality of reflectors such that theelectromagnetic wave may be aimed and/or controlled after theelectromagnetic wave is emitted from electromagnetic deterrent.

Still referring to FIG. 1 , processor 136 is configured to select a modeof first deterrent mode and second deterrent mode as a function of thebehavior descriptor. Each behavior descriptor may be associated in adatabase or other memory of and/or accessible to processor 136 with adeterrent mode and/or deterrent action as described above. For instance,a lowest behavior descriptor, such as a behavior descriptor associatedwith a person entered in a “friendly file,” a resident of a house to beprotected, an employee engaging in actions within a scope of employmentthereof, or the like may be associated with no deterrent response. Abehavior descriptor corresponding to an accidental and/or casualtrespasser may be associated with a startle action and/or other warningdeterrent such as an audio output directing subject 308 to leave subjectarea, stop engaging in an action, or the like. A behavior descriptorcorresponding to persistent trespassing and/or some initial aggressivebehavior may be associated with a more stringent deterrent, such as aglare action, louder and/or more unsettling noises, strong smells, orthe like. Extremely aggressive behavior and/or presence of a blacklistedsubject 308 may be associated with stronger responses such asafterimage, saturation, and/or strobe actions, electric shock,disorienting sounds, or the like. Processor 136 may command the directedlight deterrent 156 to perform an action of the first deterrent actionand the second deterrent action as a function of the mode.

In an embodiment, and further referring to FIG. 1 , one or more types ofdeterrent may be combined simultaneously and/or sequentially to create aflanking effect. For instance, optical deterrent actions may be combinedwith startling noises and/or commands to vacate subject area and/orcease one or more activities. Alternatively, if subject 308 has recentlyhad vision temporarily impaired, directional audio outputs may be usedto urge subject 308 toward an exit and/or to increase disorientation ofsubject 308.

In embodiments, and still referring to FIG. 1 , two or more deterrentmodalities may be coordinated using timing to compensate for differentdeployment speeds. For instance, sonic deterrents may take longer todeploy than optical deterrents. To compensate for this, in anembodiment, directed light deterrent 156, and or other light-baseddeterrent or radiation-based deterrent devices may have a built-in delaywhereby circuitry driving such devices uses a timer to wait so as tocoordinate output with associated sonic outputs. As a non-limitingexample, it may require 2-10 milliseconds for a high sample rate in theorder of 16K to be routed through most computer DSP platforms.Furthermore, sound propagates at a lower rate of velocity through airthan light; thus a delay built into a directed light deterrent 156 orother optical or radiation-based deterrent may also account for thedifference in propagation particularly for entrances who are at agreater distance from apparatus 100. In an embodiment, and withoutlimitation, two or more deterrent modalities may be a light deterrentand a directed sound source, wherein the directed sound source emits anon-lethal sound such as a firing noise and/or emission noise, whereinthe light deterrent interacts with the subject. As a non-limitingexample, a directed sound source may be emitted such as a gun fire,wherein the laser interacts with the subject. In another embodiment, andwithout limitation, a deterrent of the two or more deterrent modalitiesmay be modulated as a function of the intensity of the two or moredeterrent modalities. For example, and without limitation, a directedsound source may be louder and/or softer as a function of the intensityof a laser that is emitted. As a further non-limiting example, a lightmay be brighter and/or dimmer as a function of an intensity of anelectromagnetic wave. Additionally or alternatively, two or moredeterrent modalities may identify one or more directions as a functionof landmarks of the body. For example, a landmark of a subject's eyesmay be visible, wherein processor 136 may determine the location of alandmark of a subject's ears, wherein apparatus 100 may direct one ormore deterrents to the location of the determined location of thelandmark of the subject's ears. As a non-limiting example, a sizzling orother heat-associated noise may accompany pain ray, while “blaster”noises may accompany directed light deterrent output, or the like

Still referring to FIG. 1 , deterrent effect of a directed lightdeterrent and/or other deterrent may be designed to escalate inintensity in response to escalations in behavior and/or reduction indistance from subject to item to protect. Thus, as a non-limitingexample, intensity and/or a strobing and/or pulse frequency of lightfrom directed light deterrent 156 may be varied according to a distancebetween apparatus 100 and subject 308. As a non-limiting, illustrativeexample, a pulse frequency may be configured to be 8 Hertz at 20 feet ofdistance, 10 Hertz at fifteen feet of distance, 15 Hertz at 10 feed ofdistance, and/or 20 Hertz at 5 feet of distance. Bursts of light fromdirected light deterrent 156 may alternatively or additionally increasein length and/or intensity with closer approach, as permitted by MPEand/or other safety considerations as described above. Alternatively oradditionally, bursts and/or pulses may increase in frequency whiledecreasing a duty cycle so that there is no increase in power deliveredand/or a smaller increase in power than would otherwise occur.Similarly, outputs from audio output devices such as directed soundsource 164 may increase in frequency, volume, aggressiveness of contentas subject behavior and/or proximity escalates.

Further referring to FIG. 1 , a sequence of escalations related tobehavior descriptors may begin at a point along sequence selected by aninitial behavior descriptor determination; for instance, an initialpoint for a subject showing low aggression, and/or a subject initiallyentering with a high temperature, may be a quick flash of light fromdirected light deterrent 156 and/or an audio warning such as “back up,”or “please do not enter” at an ordinary speaking volume, while aninitial point for a subject running into subject area, brandishing anobject, or otherwise representing a more aggressive profile may includea more aggressive initial response such as a startle, glare, and/orsaturation response, a strobing or other pulsing at some rate along anescalating scale as described above, a loud, startling, and/oraggressive noise, an emission of a chemical deterrent, and/or anelectric shock, depending on a degree of aggression determined. In anembodiment, and without limitation, an audio warning may include one ormore unique and/or recognizable tones. For example, and withoutlimitation, unique tones may denote one or more tones associated with astern, commanding, authoritative, and the like thereof tone. As afurther non-limiting example, unique tones may include one or morefamous tones such as law enforcement, military personnel, celebrities,actors, singers, and the like thereof. In another embodiment, andwithout limitation, audio warning may include one or more instructionsand/or commands such as directions to leave and/or conduct a behavior.For example, and without limitation, audio warning may denote “hey youwith the watch and the glasses, drop the backpack or leave,” wherein theaudio warning may further denote a specific deterrent and/or action thatapparatus 100 may perform should the subject not head the audio warning.

In an embodiment, and further referring to FIG. 1 , text to speech canuse machine-learning models, neural networks, or the like that aretrained using training examples including voices of particular persons,to imitate those voices; so trained, such elements may enable apparatus100 to generate text to speech output imitating such voices, usingranges of expression and/or volume, or the like. Apparatus may haveenough resolution to look where a subject is in a room, what the subjectis wearing, and the like, and then generate text, and thus speech,referring to such characteristics, such as without limitation “hey youwith the watch and the glasses, drop the backpack or leave.” This mayhave the effect of imitating a human being such as a police officer,creating an illusion that security personnel is present. In anembodiment, imitation of voices may include imitation of prosody,intonation, raised voices, different emotional expressions, or the like.Given enough samples from a voice as training examples, amachine-learning model and/or neural network may be used to transfertext into voice output that has various levels of expression, or thelike. For instance, a text output may be generated having a givenexpression or intonation, such as an angry, authoritative, or othervoice.

Still referring to FIG. 1 , two or more deterrent modalities may becoordinated using timing to denote a location and/or direction of thedeterrent. For example, and without limitation, a first directed soundsource may denote that a laser and/or electric shock may originate froma first location, wherein the laser and/or shock is then subsequentlyemitted from the first location. As a further non-limiting example, afirst directed light deterrent may illuminate a location and/or areawherein the location and/or area may emit a deterrent of the pluralityof deterrents.

In an embodiment, and still referring to FIG. 1 , apparatus 100 mayoutput deterrents in various different combinations. Each combinationmay include a distinct combination of deterrent outputs, includingwithout limitation different combinations of deterrent types such aslight, audio, electromagnetic, chemical, and/or other types, as well asdifferent deterrent output types within deterrent types, such asdifferent colors and/or patterns of light, different sounds such asvocalizations, instructions or the like, different orders of output,and/or any other combination of variations that may occur to a personskilled in the art upon reviewing the entirety of this disclosure. In anembodiment, apparatus 100 may track past deterrent output used with aparticular subject, and produce outputs that differ from past outputsand/or emulate a maximally effective past output. Alternatively oradditionally, elements of deterrent outputs may be selected randomlyfrom various possible choices. In an embodiment, this may preventsubject from becoming acclimatized to any particular deterrentcombination, retaining a deterrent effect of deterrent combinations thatare output.

Still referring to FIG. 1 , deterrents from any one deterrent componentand or any combination of deterrent components may fall along a spectrumfrom low severity and or warning level responses to higher severity orstronger responses, along which spectrum responses may escalate, forexample as subject comes closer to an item to protect, fails to vacatesecure area, or becomes more aggressive, among other examples. In anembodiment, and without limitation, a spectrum response may be monitoredas a function of a servo feedback such as a microphone and/or imagingdevice that measures one or more audio signals and/or visual signalsfrom subject 308 and identifies the response of subject 308. As anillustrative example, a subject who walks into subject area at a time inwhich people are not permitted in subject area may initially receive averbal warning to vacate the area, delivered at an ordinary speakingvolume; if the subject persists in remaining in subject area and orproceeds further into subject area and trend may receive a louder ormore strongly worded warning, which may for instance be accompanied, forinstance, with a flash or startle action from directed light deterrent.If subject proceeds still further, or if at any point in time begins tobehave aggressively, or to brandish a weapon, and response may bestronger still, such as a glare, a very loud noise, a burst from themicrowave source, or the like. Strong microwave bursts, electric shock,or extremely loud noises may be used for an especially aggressive and/orpersistent subject. In an embodiment, and without limitation, a firstlow severity deterrent may include an electronic gate and/or lightcurtain, wherein a subject is refused entry to one or more subjectareas. In another embodiment, and without limitation, electronic gateand/or light curtain may be outlined and/or presented to subject 308 toat least denote an exit path and/or safety route. Alternatively oradditionally, laser writing and/or pictures on surfaces may directsubject and/or other persons to inform them which way to move.Electronic gate technology may be used, without limitation, at vehiclecheckpoints.

Still referring to FIG. 1 , in embodiments, apparatus 100 may operate asan intelligent gate, which may alternatively be referred to as a “smartgate,” which can keep out people who are not authorized to enter, whileallowing people who are authorized to enter, or people identified astheir guests, to enter the gate. Intelligent gate may simultaneously letthrough people who are authorized to enter while interdicting those whoare not. In some situations because of this ability, intelligent gatemay enable someone to enter instantaneously while providing them with alevel of protection commensurate with a physical gate. In other words,this gate may enable someone to flee a potential pursuer or otherdifficult situation by running straight through the gate as if it waswide open while the gate behaves as if it were closed uptight for aperson who is not meant to enter. In some situations, this means thatsmart gate does a superior job of protecting the persons, objects, andproperty that it is meant to protect.

Continuing to refer to FIG. 1 , smart gate may use any processesdescribed in this disclosure to determine who is to be admitted and whois to be excluded. For instance, smart gate may have a whitelist orfriend list describing persons who are generally authorized to enterthrough smart gate and/or who have been authorized temporarily to entertheir end temporary authorization may be timestamped and may beassociated with a time limit which may be expressed in any unit of timeand counted down using a watchdog timer references to external timesources, such as network-connected sources, or the like. Friend list maybe updated dynamically; for instance a person who has been recognized ason the friend list may be able to indicate that another person who iswith them is also on the friend list using a gesture, a word, or anyother suitable means of communication, which gesture, word or the likemay be recognized by apparatus 100. In some cases, a person on a friendslist may not be authorized to designate another friend. In other wordsfriends list may have more than one level of entry, which may be whichmay distinguish between people who are on the friend list eitherpermanently or temporarily, and people who are on the friends list andcapable of introducing other people. Status on a friend list that may betime limited may include status that enables a person to designate otherpeople who are on the friend list. Smart gate may recognize a person asbeing on friend list in any suitable manner, including using facialrecognition, behavior or other classifiers, or any other means that maybe used to distinguish one person from another person. Alternatively oradditionally, smart gate may use voice recognition to identify a personas being on friend list, or may accept a verbal and/or writtenauthorization password or code from a person. Verbally submittedpasswords or codes may be converted to text using speech to textprocesses as described elsewhere in this disclosure. Alternatively oradditionally, system may use biometric authentications, which mayinclude any fingerprint retinal scan or other biometric data. As afurther non-limiting example, smart gate may use communication with acomputing device, such as a mobile phone and/or smart phone, on theperson of a user to identify the bearer of that device as being on afriend list.

Continuing to refer to FIG. 1 , intelligent gate may also operate ablacklist or excluded persons list. This may include people who havebeen specifically identified by name, and/or by appearance, or any otherindicia of past interactions. In an embodiment, a person who is not onfriends list but who is detected as behaving in an aggressive orotherwise problematical manner, for instance by behavior classifier, maybe added to a blacklist and excluded until such time as they areremoved. Membership on a blacklist may be revoked, as may membership onfriends list, by persons who are on the friends list, and who haveauthorization to provide such revocations and/or changes to otherpeople/status to the smart gate and/or apparatus.

In an embodiment, apparatus and/or smart gate may use any means and ourprocess of interdiction as described in this disclosure to excludepersons who are not permitted to enter smart gate. For instance, adirected light deterrent may be aimed at, and potentially only at,persons who are to be excluded while avoiding targeting persons who arenot excluded. This may be performed using any masking and/or targetingprocesses described herein. Likewise, directed audio deterrents may bedirected at persons who are excluded informing them that they are notallowed and/or producing noises of sufficient volume to deter them. Painrays, and/or microwave energy sources, as described in this disclosure,may be used to exclude people who are not authorized to enter as well.Any or all of these processes and/or devices may be combined solely tofocus on a person who is to be excluded while avoiding harming ordeterring a person who is allowed to enter.

In some embodiments, and continuing to refer to FIG. 1 , a person who isallowed to enter smart gate may be provided with instructions, visualindicators such as arrows and/or signs drawn on the ground or on othersurfaces using directed light deterrent, audio cues informing them whichway to go, light curtains guiding them along a path, indicators guidingthem along a path, or the like; for instance and without limitation avoice output directed at a person who is permitted to enter gate maydirect that person to lie down, to run forward, to move to one side orthe other, or otherwise in order to aid smart gate in interdicting aperson who is not permitted to enter and to aid the person who ispermitted to enter in avoiding interdiction and an unwanted visitor whois to be interdicted.

Still referring to FIG. 1 , smart gate may alternatively or additionallyinclude a physical gate, which may be automatically operated using oneor more actuators, motors or the like, and may be activated to closewhen a person who is permitted to enter the gate has passed through thethreshold, while a person to be interdicted remains outside. In thiscase an ability of apparatus to buy time while waiting for backup orother additional resources to be deployed may be used to give a persontime to enter the smart gate while excluding other people until such atime as the smart gate may engage a physical barrier to prevent furtherentry. Physical barrier may alternatively or additionally automaticallyopen to allow entry or escape of a person who is permitted to passthrough smart gate.

In an embodiment, and with continuing reference to FIG. 1 , smart gatemay have emergency override functions. For example, when there is a firealarm, smart gate may be designed to prevent all persons to evacuateproperty that is guarded by smart gate, while permitting any persons whomay be entering the property to fight a fire to enter through the smartgate, whether they are currently on our friends list or not.Alternatively or additionally, smart gate may use visual, behavioraland/or equipment classifiers to determine whether a person attempting toenter the smart gate during an emergent situation is likely to be lawenforcement, search and rescue, firefighters, or other personnel who canhelp in the crisis. Alternatively or additionally, persons who areauthorized to help in emergencies may be identified on a friend list, ormaybe given pass codes, transponders, or applications on mobile devices,that can be used to signal to the apparatus that those persons should bepermitted to enter.

In some embodiments, and still referring to FIG. 1 , smart gate may beprogrammed to identify situations that require human intervention. Forinstance, where a person who is not permitted in smart gate isespecially persistent, or engaging in passive noncompliance, smart gatemay signal a human, such as without limitation a person from theirfriend, a person who is within the property, and/or a security worker,as identified using any of the systems center modalities foridentification of people as described above or the like to enable such aperson to override and or further instruct smart gate to deal with asituation. Likewise, smart gate may be able to identify when it isunable to identify a person, for example when identification of a personhas is associated with a confidence level below a given threshold. Insuch a situation, smart gate may provide a video feed to a deviceoperated by a user on their friend list, security personnel, or the likepermitting that person to make a decision regarding whether a personthat smart gate has been unable to identify is to be admitted.

In some exemplary embodiments, and still referring to FIG. 1 , smartgate may have a mode in which it operates to regulate guests to anevent. For instance, smart gate may be designed to have a friend listthat is specific to the event, a list listing people who are permittedto enter, and/or times at which different parties are permitted toenter. Smart gate may, for instance, inform a person who is trying toenter before the time of their reservation that they are excluded and/orthat they have to wait until their entry is permitted. Smart gate mayhave a number of very gradually escalating prompts to provide to aperson who is attempting to enter early, and or late, to an event so asto avoid especially harsh interdictions of person who is a potentialcustomer, if not currently permitted to attend a particular event.Similarly, a person who is behaving especially belligerently,inebriated, or persistently attempting to quote crash and quote an eventto which they are not invited, may be submitted may be subjected to moresevere interdiction, such as startle, glare, or other outputs directedby the eyes, use of the microwave pain ray, and/or use of especiallyloud noises or threats of violence.

Still referring to FIG. 1 , smart gate may, in some embodiments, beconfigured to guide persons to events to which they are authorized toattend. For instance, smart gate may use light curtains, audioindicators, and/or any visual indicators to guide persons to an eventthey are permitted to attend. In an embodiment, smart gate may usedirected light to provide a spot or other indicator that a particularperson is authorized to follow. For instance, indicator may have a colorspecific to a particular person, and/or may list their name or otheridentifying information. Where there are a plurality of apparatuses in avenue, each apparatus may receive from another apparatus a locationand/or status of a person who is to be guided, as well as the particularindicator which is meant to guide that person, so that as the personmoves from a range of one apparatus to a range of another down a hallwaythrough a corridor through a series of rooms or the like; apparatusacting as a mesh network may be able to continue to guide the personalong their prescribed path. Light curtains and/or other visual indiciamay also be used in a similar fashion. System may warn a person who isstraying from their designated path that they are entering dangerous orunauthorized areas, such as areas designated for different parties thanone to which they are to attend, and or areas where construction orother activities that could endanger them may be taking place. Likewise,indicators and/or any level of interdiction may be used to prevent aperson from accidentally or intentionally entering into areas operatedby venue personnel and not accessible to the public, such as kitchens,orchestral pits, stages, or the like

Still referring to FIG. 1 , embodiments described above may be extendedto other situations; for instance, where construction is taking place,apparatus may indicate safe paths for people to travel when not wearingprotective equipment, and/or may generally interdict and or warn offpersons who are attempting to enter an area without protectiveequipment. Image classifiers may be used to detect whether someone iswearing correct protective equipment in a construction area, or whenabout to enter it. For instance, where hearing protection, headprotection, and/or visual protection is required in a given constructionarea, to prevent, for instance, hearing loss, head trauma, and/or damageto the retina's from welding equipment or the like, system may informsuch people that they are not permitted, attempt to interdict them fromentering using any form of interdiction described in this disclosure,and/or transmitted message to security personnel and/or other personsworking in the construction zone to prevent entry of the unprotected andor unauthorized person.

As a further non limiting example, and still referring to FIG. 1 , smartgate may be used to exclude from a stage area persons who have not beenpermitted onto the stage, for instance during a concert such as a rockconcert. Apparatus 100 may be configured to detect when a person who ison a stage, such as a performer, has invited a particular person ontothe stage. For instance, where a performer is inviting a member of anaudience to come up onstage, such performer may be able to indicate witha gesture a word or the like that a particular person is permitted onthe stage. This may operate in any of two ways the first, is byindicating that a particular person is permitted to enter on the stage.In some cases, apparatus may cast a light on that person, for example ontheir chest, and await a verbal or other confirmation such as gesturalconfirmation by a performer that the person is permitted onstage. As analternative, apparatus may suspend interdiction functions for a shortperiod of time upon indicated by a performer, relying on securitypersonnel to maintain order while a particular person is allowed bysecurity personnel to enter the stage.

Still referring to FIG. 1 , apparatus 100 may have an interlock safetysystem 192, that consists of one or many safety features that use sensorand other data to determine safety of the system. This may include, butis not limited to, laser energy measurement devices, scanning mirrormovement detectors, power usage, distance measurement, “trip wires,”disturbance sensors (e.g., subject 308 physically shakes apparatus 100),enclosure opening, current or voltage monitors, microwave/MMW energysensors, microphones, accelerometers to measure projectile velocity,pressure sensors, and/or other methods to detect safety or efficacy ofthe system. The processor 136 in analyzing this data may use interlocksto interrupt deterrents in order to prevent injuries, violations, orother negative consequences. This may be accomplished with methods, suchas, but not limited to, optical shutters, filters, attenuators, powerswitches, physical interruptions, current/voltage limiters, and/or othermethods to inhibit the performance of systems or subsystems to maintainsafe operating parameters.

Still referring to FIG. 1 , apparatus 100 may have a battery, 196, whichmay act as a primary power source and/or as a backup power source toprovide additional operating time if power is interrupted to the system.Battery may include one or more battery elements in parallel and/orseries configured to provide power components of apparatus 100. Forexample, battery may include one or more lithium-ion batteries, alkalinebatteries, lead-acid batteries, aluminum-ion batteries, flow batteries,magnesium-ion batteries, metal-air electrochemical cells, nickel-ionbatteries, zinc-ion batteries, or any combination thereof, to name afew. According to embodiments, battery may include an alternative powersource such as an alternating current (“AC”) power source, directcurrent (“DC”) power source, power over ethernet (PoE), a solarphotovoltaic cell, a wind turbine, or any combination thereof, and/orpower electronics such as a half-bridge rectifier, full-bridgerectifier, inverter, maximum-point power tracker, power converter (suchas a buck converter, boost converter, buck-boost converter, flybackconverter, transformer, etc.), or any combination thereof, to name afew. According to embodiments, battery may be configured to providepower to one or more elements of apparatus as described in furtherdetail below. This may be accomplished using power management circuitryincluding, for example, a power microcontroller, switches, relays,transistors, linear regulators, power converters, or any combinationthereof, to name a few.

In an embodiment, and still referring to FIG. 1 , apparatus 100 may beconfigured to perform a training protocol. As used in this disclosure a“training protocol” is a method and/or scenario that allows anindividual to track and/or follow a projectile as a function of aplurality of emitted deterrents. For example, and without limitation,law enforcement may enter a subject area, wherein a light directeddeterrent is emitted at a location, wherein the law enforcement fires aprojectile towards the location at which the light directed deterrent isdirected. Apparatus 104 may emit a plurality of deterrents to signal alocation at which the projectile interacted with the location and/or tosignal a location at which the projectile missed the location. Forexample, and without limitation, apparatus may project a target and/orpoint on a wall, wherein a golfer may hit a projectile towards thetarget, and wherein apparatus may utilize light radar component 116 toidentify a distance at which the projectile missed the target and/orpoint on the wall. In an embodiment, and without limitation, apparatusmay track one or more projectiles located within subject area. Forexample, and without limitation, a golfer may hit a golf ball, whereinapparatus 100 may track and/or identify a plurality of projectileelements. As used in this disclosure a “projectile element” is anelement of datum associated with a projectiles kinematics. For example,and without limitation, apparatus 100 may track and/or identify a golfball's velocity, spin, launch angle, landing angle, peak height, and thelike thereof. In a golf context, apparatus may be able to tracks wherethe balls went. Apparatus may track everything there is about a golfball, golf shot, and perform shot-by-shot analytics. Detectiondirections from which a projectile and/or shot has emanated may includedetection of sound pattern to detect whose gun just fired by trackingand/or detecting a “crack-bang” noise of a shot, sensing a muzzle flash,or performing millimeter-wave detection of bullets.

Further referring to FIG. 1 , apparatus 100 may be configured todetermine a source of a projectile and/or beam. For instance, andwithout limitation, imaging device may be used to determine a trajectoryof a bullet or other projectile. Apparatus 100 may determine a source ofa bullet, projectile, or the like, where a “source” is a location and/ordevice from which the bullet, projectile, or the like may have beenfired, launched, or the like. Apparatus 100 may determine a terminus ofthe bullet or other projectile, where a “terminus” is landing point,target, and/or endpoint of bullet or other projectile. Apparatus 100 mayuse a visual or other indicator to indicate a trajectory, source, and/orterminus of a bullet and/or projectile; for instance, and withoutlimitation, apparatus 100 may use directed light deterrent to place adot or other marker illuminating a source, target, and/or one or morepoints along a trajectory and/or a projection thereof on one or morephysical surfaces; for instance markers along ground, buildings, walls,or the like may illustrate trajectory, while a spot, marker or the likemay indicate a source and/or a location nearby. This may be performed,without limitation, in an “active shooter” and/or combat situation,permitting a user to locate an origin of bullets and/or otherprojectiles, and thus identify choices for cover, and/or target asource, or the like. Apparatus 100 may alternatively or additionallyemit one or more deterrents at source. Use of a marker to highlightpersons and/or items may include use of the marker to highlight one ormore persons behaving erratically and/or problematically, such as twopersons engaging in behavior indicating mutual aggression and/or alikely impending and/or current altercation, a person who has engaged inany potentially criminal and/or harmful behavior, harassment, sexualharassment, or any other person who may be behaving in a problematicmanner in any way that may occur to persons skilled in the art uponreviewing the entirety of this disclosure. As a further non-limitingexample, a group of soldiers, marines. Or the like in an urbanenvironment who are pinned down by a larger force may each have anapparatus that they stake into the ground; apparatuses may be meshable,so they communicate with each other, and may perform one or more actionsas described herein, for instance blinding people who are trying totarget combatants while also informing them of antagonists' positions,providing targeting data, or the like. Apparatus 100 may place a “scope”spot, such as with a red laser, on a selected target, where target maybe selected according to any threat-assessment and/or prioritizationprotocol as described herein. Scope spot may alternatively oradditionally be in an invisible wavelength which may be visible ingoggles or other displays visible to a user of apparatus 100. Apparatusmay alternatively or additionally be provided with and/or store datadescribing one or more mission objectives. For instance, where apparatuswhen a mission objective includes finding a particular person, object,or building, apparatus may use image classification to aid inidentification thereof. Similarly, apparatus 100 may identify routesand/or hazards along routes, and may provide guidance for combatants totraverse such routes.

In some embodiments, cameras and/or imaging devices of apparatus may beused to detect threats and act on them, such as a person using a laserpointer to blind cops, vehicle operators, other cameras, or the like.Apparatus may track people using a threat and/or laser pointer and/ormay determine which group of people is to be protected, and which groupis using threats such as without limitation laser pointers on them.Apparatus may then disrupt and/or track an identified malefactor, forinstance using video handing off information to people that need toknow, visible or IR laser pointer on the malefactor follow them incrowd, and/or use of any countermeasures described herein to disruptmalefactor's use of pointers and/or other harmful measures. Similarmethods may also track people that may use mace or tear gas sprays,paintball guns, or the like, for instance using source identification asdescribed in this disclosure.

Alternatively or additionally, and still referring to FIG. 1 apparatusmay identify situations where apparatus and or combatants are unable torespond to hazards and are enemy combatants that present themselves. Forinstance, apparatus may use projectile detection to determine that athreshold number of projectiles are incoming, or that a number ofincoming projectiles exceeds outgoing projectiles from combatants usingapparatus by some threshold amount, indicating that combatants may beoutnumbered. Apparatus may automatically open a communication channel toa command center and/or to backup forces permitting combatants to callfor help, and/or may actually transmit such a call for helpautomatically. Apparatus may also use targeting and/or source detectionprotocols to transmit information to a complementary force such as adrone, Air Force air support, or the like, permitting an airstrike orother targeted action to be performed against an enemy combatant thathas been identified.

In an embodiment, and continuing to refer to FIG. 1 , apparatus mayalternatively or additionally interfere with enemy combatants' abilityto engage compounds, for instance by obfuscating or making moredifficult one or more senses of the enemy combatants. This may beperformed using dazzle and/or glare effects with light, and/or mayinclude generation of sound at a sufficient volume at the location ofenemy combatants to interfere with communication there between. This maybe used to decrease the combat efficacy of enemy combatants and providecombatants with an ability to get an upper hand, may be used to provide“cover” to combatants so that enemy combatants may temporarily beengaging apparatus at first location while combatant move into a secondlocation to outflank or otherwise outmaneuver enemy combatants.Alternatively, apparatus 100 may use any or all interdiction techniquesdescribed herein to protect and or provide cover for combatants that areattempting to evacuate, or retreat from enemy engagement.

Referring now to FIG. 15 , an exemplary embodiment 1500 of anarchitectural layout for apparatus 100 is illustrated. Driver circuitry1504 which may be used to direct timing and synchronization of opticaldevices such as directed light deterrent 156, light radar device, and orSonic or other deterrent devices, may be rendered using hardwareprogramming such as without limitation ASIC, microcontroller, and/orFPGA designs. Such circuitry may perform scan timing, modulation, andintensity settings, including without limitation pulse on and off timingfor directed light deterrent 156 within one or more electromagneticbands, including, but not limited to, visual light, as described above.Intensity, pulse-width, pulse frequency, and/or x/y referencecoordinates and/or ranges may be by inputs from processor 136 asdetermined according to behavior descriptor determinations, deterrentselections, detected and/or predicted locations of a subject 308 and/orone or more anatomical features such as without limitation visual band,or the like.

Still referring to FIG. 15 , driver circuitry 1504 may perform scanningcommands for directed light source (laser), microwave ray, sonic aiming,setting of intensity, and/or focal length, or the like. Driver circuitry1504 may record ToF detections and/or timing, ToF edge detection,retinal retroreflection, optics detection, or the like. Driver circuitry1504 may update scanning ranges without processor 136 input based ondetected changes in edge position and/or object position based on ToFmeasurements or other sensor measurements; processor 136 may furtherprocess and/or correct scanning ranges and override initial changes,permitting a very rapid response to be tempered by data integration,analysis, and/or prediction at a processor 136 level. As a non-limitingexample, driver circuitry 1504 for directed light deterrent 156 mayinclude an XY2-100 controller interface, which operates on fourdifferential signals: x position, y position, clock, and sync signals,to govern scanning apparatuses such as galvanometric reflectors or thelike, or control non-mechanical beam steering apparatus to point thelight or other deterrent sources. Driver circuitry 1504 may furtherinput received sensor signals and/or perform initial signal processing,filtering, or the like. In an embodiment, driver circuitry 1504 mayinput intensity readings as described above for ongoing monitoring ofintensity levels of directed light deterrent 156, which may be relayedto processor 136 and/or remote device 140. Firmware updates may bereceived from processor 136 and/or remote device 140 to modify drivercircuitry 1504, for instance for improvements to tracking, power usage,image and/or signal processing, reductions in power output for MPElimitation, or the like. Apparatus 100 may update coefficients, weights,biases, and/or other elements of any machine-learning model and/orneural network.

With further reference to FIG. 15 , driver circuitry 1504 may include,without limitation, an auto calibration engine 1508 for galvanometer orother scanning optics by laser beam firing and computer vision (CV)obtaining location data with relative respect to the imaging device 104.Driver circuitry 1504 may include a raster generator engine 1512 forscanning optics with CV window size, position, speed and number oftargets. Driver circuitry 1504 may include a blanking generator 1516 fordeactivation of light sources when outside visual band as describedabove or when not needed. Driver circuitry 1504 may include a pulsegenerator 1520 for laser voltage current, pulse width, pulse time,strobe effect, or the like. Driver circuitry 1504 may include safetyfeatures to limit the power, exposure, or use of the laser should safetythresholds be crossed.

Still referring to FIG. 15 , processor 136 may perform actions asdescribed above such as implementation of decision trees 148 or othercomputational processes for behavior descriptor determination, trackingof individual subjects 308 and/or anatomical landmarks, determination ofdeterrent response based on behavior descriptor and/or inputs fromsensors, imaging device 104, or the like, modification of deterrentresponse based on detected and/or predicted subject 308 actions, or thelike. Processor 136 may perform image generator software, includingrendering and/or signaling of raster, words, graphics, or the like fordriver circuits. Processor 136 may generate scan timing and intensitysettings to be provided to driver circuits.

With continued reference to FIG. 15 , processor 136 may receive and/orimplement one or more machine-learning models and/or heuristics, forinstance for movement trend prediction, classification, or the like.That is, where a machine-learning process produces a model that isrelatively compact and/or simple to evaluate, such as a regressionmodel, a vector or other distance-metric based classifier, or the like,the model may be loaded to processor 136 from a remote device 140 andused to generate outputs classifying images and/or behaviors, predictingmotion, or the like. Alternatively or additionally, one or moremachine-learning processes may be performed on processor 136.

Still referring to FIG. 15 , a remote device 140 may perform one or morecomputationally intensive, and/or memory storage intensive processesand/or tasks, based upon sensory input received from processor 136and/or driver circuitry 1504. Such tasks may include, withoutlimitation, machine learning processes such as generation of classifiersfor image classification, object classification, face recognition and/orbiometrics. Remote device 140 may additionally or alternatively be usedto perform database look ups and queries, for example queries todatabases containing friend lists friend files whitelists blacklistscome up or other data describing authorization levels corresponding toidentified subjects 308. Remote device 140 may alternatively oradditionally perform computer image processing tasks, such as edgedetection object detection classification or the like, for instance asdescribed above. Remote device 140 may perform image coordination and orfusion techniques, such as combinations of optical, infrared, and/or ToFimaging data, to produce integrated images having greater accuracy orwealth of information. Remote device 140 may alternatively oradditionally collect and store data from disparate apparatuses 100 thatare used by different entities. Remote device 140 may then process thisdata to determine trends, new behaviors, errors, service issues,warranty information, and/or other uses deemed valuable to enhance theperformance of the system as a whole, improve user experiences, improvedeterrent effects, improve reliability, and/or other benefits.

Still referring to FIG. 15 , remote device 140 may and alternatively oradditionally communicate with further devices under services, such ascloud services, which may perform, without limitation, cross platformdata aggregation and/or analysis, data storage, or other tasks, such asupdated safety regulation and/or settings, as well as software, FPGAand/or firmware updates. Find stamps, and without limitation, remotedevice 140 and or processor 136 may regularly, iteratively, arecontinuously, update stream and or otherwise provide video, sensor, andother data, to one or more remote services such as cloud services.Alternatively or additionally, processor 136 and/or remote device 140may be configured to transmit alerts to users, such as silent alarms orother security data that a user may be able to utilize in taking actionin response to a perceived or sensed security threat. Remotecommunication may further be used to contact law enforcement and/orsecurity services which may coordinate their efforts with actions takenby apparatus 100. Security services may be provided with safetyequipment and our overrides, so that they made either deactivateresponses arrival area, and or maybe a responses by, for instance,eyewear having dichroic lenses or other optical elements to protectsecurity personnel from directed light deterrent 156 actions or thelike.

With continued reference to FIG. 15 , it should be noted that theabove-described architectural distributions of tasks are provided forexemplary purposes only and are not intended to be limiting. Forinstance, and without limitation, processor 136 may perform one or moretasks described above as performed by driver circuitry 1504, such asscanning timing and order image reception processes. Similarly,processor 136 may perform one or more actions described above asperformed by remote device 140, and or cloud services, depending onComputational and are storage resources deployed with or at processor136. Alternatively or additionally, remote device 140 may perform one ormore processes described above for processor 136 and or driver circuitry1504, for instance using a thin client architecture for many of thetasks to be performed by apparatus 100.

Still referring to FIG. 15 , one or more tasks performed by apparatus100 may be evaluated or overwritten, or else performed by a human in theloop, who may be a user, to whom one or more determinations fromabove-described processes, threat responses, threat determinations,identifications, or the like maybe provided. User may in turn entercommands, authorizing, overriding, and/or specifying threat responsesidentifications or the like. For instance, user may include an operatorof a security desk, such as a security desk in a school, mall, or otherinstitution, where proprietors may be interested in preventingaccidental harm and or distress of innocent persons such as studentsunder customers, it may need or require based upon regulations a humandecision maker to be a part of any security response protocol. A humanin loop may be provided with an option to engage with lethal force,which may not be enabled without human authorization. In someembodiments during an initial customer deployment, apparatus may beconnected to a manned monitoring center. Monitoring center may provideoversight, operational redundancy, communications and/or direct accesswith a local police department and/or security detail. In someembodiments, manned monitoring centers may be continually orperiodically employed, such as with K-12 schools.

Still referring to FIG. 15 , two or more apparatuses may communicate,either directly, for instance through wired or wireless communicativeconnections, and/or indirectly, for instance via remote device 140, orother services, to form a mesh network. Mesh networks may be used tocoordinate responses between two or more apparatuses. For instance, twoapparatuses in the same subject area may coordinate transmission ofdirected light deterrent 156 actions, or other actions based upondetected user subject 308 behavior, postures, or the like. For instance,and without limitation, two or more apparatuses may have two or moredeterrent light wavelengths which may be deployed concurrently orsequentially in order to add to confusion and/or resistance to eyewearprotection as described above. Alternatively or additionally, two ormore apparatuses deployed in two or more separate security zones and/orsubject areas may coordinate by communicating actions and/ordeterminations concerning entrance and/or intrusions in such securityareas. This may be used, for instance, to determine what ambient lightexposure a subject 308 has experienced, which direction the subject 308has come from, and/or what activity subject 308 may be expected toperform. For instance, where one apparatus 100 has detected aggressivebehavior by subject 308, this may be used as an immediate blacklist byother apparatuses, where a subject 308 identified as the same subject308 entering a new subject area may be immediately responded to withmore aggressive responses such as saturation, strobing, electric shockor other responses, on the basis that this subject 308 has beenidentified as a threat that must be neutralized. Such data may also betransmitted remotely, and sent as updates to security teams, lawenforcement, or other users attempting to respond to an ongoing ordeveloping security threat. Such user may use such information todetermine a likely current location of a perpetrator and or othersubject 308 as well as to formulate or plan a strategy for counteractingthe actions of the subject 308 and neutralizing any threat. Two or moreapparatuses deployed in the same area may be used to create one or moreadditional coordinated actions, such as creation of light curtains, toindicate divisions between authorized and unauthorized areas, guidecrowd movement, or the light. As a further example, a series ofapparatus is may provide directional indicators such as directionalimages or the like which made direct entrance and our users in DirectionEvacuation, or the like.

Still referring to FIG. 15 , apparatus 100 may deploy any suitablearchitecture and/or protocol for intercommunication with other devices.One such architecture may include edge/individual architecture;edge/individual architecture may include a single apparatus. Apparatusmay operate in this mode by itself autonomously or semi-autonomously(user defined) with on-board AI. A user my choose whether to getfirmware and/or AI updates via “over the air” or through other secureupdate methods; alternatively or additionally, apparatus 100 may beconfigured to receive such updates according to a default protocol,which may be factory present or the like. A single system may have nocountermeasures, for instance acting similarly to a smart securitycamera, and/or may have one or more audio, optical, and/or othercountermeasures as described in this disclosure under its control. Anindividual apparatus may store, analyze, and/or archive data, videos, orthe like securely for later retrieval, processing; storage may be localand/or may be performed at a remote device, cloud system, or the like.

With further reference to FIG. 15 , an apparatus 100 may participate inand/or instantiate on-premise/distributed architecture, in which severalindividual systems may be networked together to share information, data,models, countermeasures, sensors, or the like; in some embodimentsapparatuses in such a configuration may interact with and/or share withother companies' sensors such as without limitation ONVIF enabledsecurity cameras, countermeasures such as automatic vehicle barriers,and the like and/or data recording systems. This may be accomplishedusing any suitable topology that may occur to a person skilled in theart upon reviewing the entirety of this disclosure.

Alternatively or additionally, and still referring to FIG. 15 ,apparatus may be deployed in a point to point, bus, hybrid, mesh, orsimilar network, where each apparatus may be directly communicating toeach other apparatus, and/or wherein an apparatus acting as a primarysystem may be chosen manually by a user or automatically by AI;selection of a primary system may be dynamic. Alternatively, a meshnetwork may be a network of coequal devices, which may individuallyand/or in concert react to threats and/or incidents on an event-drivenbasis.

Still referring to FIG. 15 , apparatus 100 may participate in anarchitecture that is controlled through a centralized topology, such asa star, bus, and/or hybrid network topology, a topology with one or morehub(s) with hubs including a hardware appliance and/or software thatruns on a server, or any other computing device as described herein,that collects, amalgamates, processes, and disseminates decisions, data,models, threats, alerts, or the like. A hub may use data from systems auser, customer, and/or proprietor owns; hub may process video,incidents, or the like to share threats across a system and/or networkincluding apparatus 100 so the system and/or apparatuses or otherdevices therein may coordinate countermeasures, prepare for threats,share countermeasures, or the like. In an embodiment, data may also helpimprove performance for apparatus and/or network thereof based onoutcomes.

Still referring to FIG. 15 , apparatus and/or a system incorporatingapparatus may be controlled autonomously or semi-autonomously fromcommand-and-control operation centers with people if desired.

With further reference to FIG. 15 , apparatus may communicated withand/or participate in a cloud computing architecture and/or system.Apparatus and/or a network thereof may connect to a cloud processingfacility that may amalgamate all data, for instance according to asingle user's site or two or more user sites, according to a commoncustomer, according to a region, or the like. Cloud processing facilitymay use this data to process better performance, learn new threats,learn better methods for countermeasures, enhance sensor performance,enhance sensor fusion, or any other improvements or corrections that maybe gained from the data. Cloud processing facility may then distributethis back to on-premises and/or edge device across an entire deployment.Human overwatch may be provided from these cloud locations to watch overseveral systems to provide person-in-the-loop, if desired. Part of dataAI and/or apparatus may store may include countermeasure effectiveness,success versus MDE, “waveform”, time of day, other recorded conditions,person characteristics (if available) such as age, color eyes, glasses,or the like, and/or any other data that may occur to persons skilled inthe art upon reviewing the entirety of this disclosure. Such data may beused to refine countermeasure use. This information may be used locally,on premise, and/or shared in the cloud.

Still referring to FIG. 15 , apparatus may be deployed in one or moreportable, and or sessile packages, including, without limitation, ahandheld device or other non-lethal weapon and/or self-defense device.Devices may be deployed in the form of a portable or projectile devicesuch as a quote flash bang and quote grenade or similar item which meansbe thrown into subject area to neutralize or surprise potential threatstherein. Where apparatus 100 is deployed as a flash bang grenade,certain analytical processes may be performed in a truncated manner orskipped, such as scanning for threat determination Biometrics, or thelike. For instance, a flash bang grenade version of apparatus 100 maysimply pulse high intensity light from directed light source in variousdirections to maximize an area of spread, in a strobed or other highintensity pattern, so as to disrupt as many people in an area aspossible. Similarly, loud noises may be broadcast in all directionscreate spread out deterrent effect. As a further example, such a flashbang grenade embodiment may be used to disperse one or more irritantsand or noxious smells as described above to further neutralize or clearan area of malefactors.

With continued reference to FIG. 15 , apparatus 100 may be deployed inone or more self-contained packages, such as a light bulb, which may beinserted into a conventional light socket, drying electrical power therefrom, and performing any or all tasks including threat detection threatdetermination, threat response, communication with other devices in anash network and our remote device 140, or the like. In an embodiment,device may further function as a conventional light bulb, using one ormore lower intensity light sources while surreptitiously, or otherwisenondestructively, performing optical and or time of flight scans ofsubject area to ensure security is in safety are maintained.

As a further example, and still referring to FIG. 15 , apparatus 100 maybe mounted to a person and/or other moving object such as a drone and/orrobot. For instance, and without limitation, a version of apparatus 100may be deployed on the person of a police officer or the like who maywear it in a position typically reserved for a body camera. Forinstance, apparatus may function as a body camera, performing allactions currently performed by a body camera, while also performingautomatic deterrent actions as described above. In some embodiments, andapparatus so the deployed system may enable a police officer to remaincalm and worry less about protecting the officer's person, whiledeciding how to respond to various situations that may arise, becausethe officers on safety may be protected, or at least alleviated by thepresence and actions of apparatus. Such deployment may maintain membersof the public at a certain distance from the police officer, orotherwise to aid in non-lethally neutralizing any threats posed by anerratically behaving and or aggressive person towards the officer. Thisadditional level of nonlethal protection may, in some instances, preventthe officer him or herself from having to become aggressive, orstressed, as a certain distance and degree of safety may be maintained.

In some embodiments, and still referring to FIG. 15 , apparatus 100 maybe deployed on the person, or on a vehicle, or both, of a policeofficer, or a team of police officers. In an embodiment, apparatus mayact as a force multiplier for police, by detecting, interdicting, orotherwise aiding in control and prevention of criminal and/orthreatening behavior. For instance, an apparatus on the person or nearto a police officer may be able to detect potential threats, and/orpeople who are approaching too close to the officer, that the officer isunable to detect. As another example, apparatus may be able to detectand interdict persons who are behaving in a problematic manner, and whoare not and or who are known to be a threat, so as to prevent them fromarriving close enough to a police officer to present a physical dangerthereto. In this way, apparatus may act to defuse potentially violentsituations before they occur.

Further referring to FIG. 15 system where worn on the person of anindividual such as an officer or the like may consist of one or more“pucks” positioned on the person to cover fields of view around theperson. This may include, as a non-limiting example, a single apparatusmounted on an individual's chest, providing a 90° or so, on theindividual's shoulder for a 270° coverage, or front and back providing360° or similar arrangements to achieve what is needed. Pucks mayinclude any elements described above for apparatus 100, includingwithout limitation one or more of the following, sensors for video,which may be visible, infrared, thermal, or the like, ultrasonic, lidar,radar, acoustic, gunshot, temperature, humidity, inertial measurementunit, gyroscope, or other sensing modalities. Pucks may also optionallyhave countermeasures built in, such as an optical, acoustical, kinetic,such as sting ball, beanbag, bolo, or the like, neurostimulation Taser,shock, or the like, chemical, such as teargas, mace, pepper spray,indelible ink, or the like, and/or any other countermeasures describedin this disclosure. These countermeasures may be steered or not. Onepucks and/or a separate device may house a CPU and/or other processorfor real-time processing of the sensor feeds. Such as system may be usedto watch a user's surroundings, and if the situation warrants, takingthe user's position, velocity, surrounding conditions, countermeasure(s)may be deployed as necessary to mitigate a situation, whether it be withanother person, animal, drone, camera, or the like. Training may also beprovided to a user to take advantage of system; for instance, if anoptical disrupter is used on a person charging the user, the user willbe trained to step aside to let the person charge past them since theycannot see them. System may also use video to time use ofcountermeasures to fire at the right time; for instance, if there is apepper spray countermeasure, system may wait to fire when a target'srange, velocity, and position are correct to improve delivery. Anotheruse may include detection of gunshots fired at the user, in which casesystem may engage the threat with optical or other countermeasures.System may also record video and/or sensor data for later retrieval.This device may give special attention to limitations of a batterypowered device and size/weight constraints for it to be on a person.Other systems on other people in the area may be authorized to assistthe user or users that are under threat; for instance, an opticaldisrupter from another user may engage a threat detected by the originaluser.

Still referring to FIG. 15 , apparatus may be mounted to and/or deployedon a patrol vehicle; such a deployment may act as a “guardian angel”device fitted on the vehicle to cover necessary FOVs. Such as system mayhave one or more sensors, countermeasures, processors, recordingabilities as described previously. Since system may be mounted on avehicle, system may have more power and size and/or weight available fordesign than a system mounted on a person. Scenarios of use may includewhen a suspect is pulled over and exits their car with a weapon; in thiscase system may engage them to disrupt them. As a further example, somepeople may try to sneak up on a vehicle, and system can hail them andask if they need something based on their motion, distance, to thevehicle or the like; if they do not respond correctly, system may thenengage them with countermeasures. This system may also extend to othervehicles and convoy use, including civilian, government, military, orother uses. For example, for a military convoy, system may provideoverwatch as a convoy is traveling. If a convoy is engaged, system maydetect threats with appropriate sensors and use countermeasures, such asoptical, countermeasures or the like, to disrupt the attack. System mayalso be used to mark threats visually with a laser or other system forsituational awareness. System may be activated for “on the move” and maytake into consideration motion of a vehicle it is attached to whileanalyzing sensors, deploying countermeasures or the like.

Generally, and still referring to FIG. 15 , a system deployed in amilitary and/or law enforcement context may have multiple potentialforms including portable, fixed, vehicle-mounted, or other forms. Forinstance, a portable form factor may be small enough to be carried by asingle or multiple people and quickly setup. Such a system may provideoverwatch in a temporary location and use sensors to detect threats,which may include without limitation people, animal, drones,surveillance equipment, or the like, and disrupt them. Such a system mayhave its rules of engagement changed depending on needs and may be nearan operator or remote from them, depending on needs. When a threat isdetected, system may engage with countermeasures to disrupt the threat.System may be networked, for instance and without limitation accordingto any networking protocol as described above, and/or usedindependently. System may possibly share data, command/control, video,or the like across data links, if present. System may also be used tocause diversions or provide communication to others. Specialconsideration may be given to size, weight, power, and datalinks toensure the system is portable and useful. System may possibly share dataamongst other similar systems or a larger security architecture toenhance its performance by sharing information, countermeasures, data,capabilities, or the like, for instance and without limitation asdescribed above.

Still referring to FIG. 15 , apparatus may be provided in a fixedsystem. A fixed system may include a system that may be fixed to alocation to provide semi-permanent or permanent overwatch, such as abuilding, perimeter, fence, wall, or the like. A system with thisconfiguration may have the capacity to have more power, longer rangeand/or more countermeasures and sensors, due to increased capacityenabled by lesser need for portability. System may be networkedaccording to any network configuration described in this disclosure.System may be used as a standalone or in tandem with other apparatuses,systems, and/or countermeasures including without limitation anydescribed in this disclosure. System may also use, employ, and/orcommunicate with systems and/or technologies controlled or operated byother companies.

With further reference to FIG. 15 , apparatus may be provided in or on avehicle; such a deployment similar to a patrol vehicle installation asdescribed above, but may be more military-specific, and may includelethal interdiction options such as ballistic weapons or the like.System may also be fixed to, installed in or on, or otherwise coupledwith an aircraft, ship, or the like. System may be used, withoutlimitation, to dazzle incoming aircraft, drones, and/or other vehiclesand/or combatants; for instance, a fighter may use an installed systemand/or apparatus to dazzle another aircraft that is dogfighting with it.

Still referring to FIG. 15 , system and/or apparatus may be deployed ina handheld, portable, and/or weapon attachment form; this version may bespecifically made to be held like a flashlight, attached to a weapon ona modular rail, and/or temporarily attached to a location. A system inthis deployment may take into consideration motion of a person or objectto which it is attached and/or threats' positions relative to it toengage them. System may cancel out motion of a user to keep sensors,countermeasures, pointers, or the like aligned to threats and/or objectsof interest. System may use its own sensors, for instance as describedabove, or use other sensors located on an object or person it isattached to.

With continued reference to FIG. 15 , apparatus may be designed todistinguish persons who present a threat, and/or persons who areotherwise problematic or need to be detained, from members of thegeneral public, and/or from members of law enforcement. For instance,face recognition classifiers, and/or other image classifiers, may detectand/or identify a person known to be a fugitive, to have an outstandingwarrant, or to present a threat, as non-limiting examples. Such a personmay be indicated beforehand using photographs, such as “wanted” posters,electronically transmitted notices indicating that a person is beingsought, is a suspect, has been reported missing, and/or is a person ofinterest in a case, or the like, and/or may be communicated to apparatusfrom a server and/or database containing images and/or classificationdata including such persons. Alternatively, an officer operatingapparatus, and/or near the apparatus, may indicate verbally with agesture or both that a particular person is a target, a person to bearrested, and/or a person to be kept at a distance. Apparatus may usedifferent interdiction modes for different categories of persons to beinterdicted. For instance, a first interdiction mode may be deployed fora person to be arrested. An interdiction mode for a person to bearrested may prompt apparatus to cast commands at that person that theyshould lie down, that they are surrounded, that they are covered by aweapon, or the like. Likewise, directed light deterrent may be used tointerfere with visual process of such a person, for instance by creatingglare as described in this disclosure. For persons who are to bearrested, body outputs may focus on disorientation pump and orprevention of affective function and or communication by that person.Lights may be used to generate stroboscopic effects, and/or othereffects calculated to disable a person, and prevent them from leaving agiven area, where they are to be intercepted. Similarly, a pain ray,and/or microwave radiation source, may be used to deter and/or disable aperson, as may audio outputs, visual outputs, or the like. Indicatorsmay inform a person verbally, or using vegetable indicia, of thedirection in which they are to travel, whether to be arrested, or tovacate an area where they are not supposed to be.

Still referring to FIG. 15 , a further category of person to beinterdicted, for instance using a different interdiction mode, orpersons who are considered a potential threat. Such persons may not bemarked for arrest, but instead may be persons for whom apparatus isconfigured to keep at a distance, so as to prevent them from engaging inhostile activity with police officers, or security personnel. Forinstance, interdiction may act as a nonlethal deterrent for a potentialattacker, while keeping such a potential attacker at bay, so that policedo not have to resort to more stringent forms of deterrence, and/orviolence.

In some embodiments, and further referring to FIG. 15 , apparatus mayuse methods to detect situations where backup is needed, and mayautomatically call for backup. For instance, apparatus may detect whenan officer operating apparatus has been disabled, and automaticallycover backup even if the officer is unable to do so personally.Apparatus may to call for backup when detecting, for instance usingclassifiers, and or a finite state machine as described above, asituation in which matters have escalated, but officer may be unable torequest backup in person. Apparatus may detect that an officer has beensubjected to violence or disabled and may call for backup in thatsituation as well. Call for backup may include, without limitation,information describing a geographical location of apparatus, such as ageofence location or the like. This way, even if apparatus is disabled,and/or if officer is on able to communicate, it may be possible forpersonnel as part of a backup process to find a location at which a mostrecent message was transmitted, permitting people responding to such acall to be able to track malefactors and provide assistance moreeffectively than if the apparatus was not involved in the interdiction.

Still referring to FIG. 15 , apparatus may alternatively or additionallyinteract with other apparatuses operated by other law enforcementpersonnel. For instance, where a group of officers are dealing with agroup of civilians, rioters, or potential criminals, apparatus maycoordinate to interdict such persons, identify them, keep them separatefrom each other, prevent cute communication there between using forinstance white noise to disaggregate them, or the like. Apparatus mayfunction to set up barriers between persons who otherwise mightcoordinate, while permitting police officers to cooperate. Similarly anapparatus operated by one police officer may transmit information aboutincidents and/or identification involving and/or of a particular personwith whom one officer and/or apparatus has interacted so that apparatusbelonging to a second officer may be able to respond effectively to sucha person. For instance, if a person has knocked down, disabled, orotherwise harmed a first officer an apparatus of that officer maydetected such occurrence has occurred for example for instance bydetecting that the officer has so Im using an inertial measurementsensor or the like, and or by using behavioral classification, and othersensor and our classification data to determine what has occurred. Inthis case, apparatus may send to ace remote server, and or to localapparatus is that are within range of apparatus, information detailingwhat has occurred. Other apparatus if so informed, may initiateescalation to a higher escalation level in a finite state machinecorresponding to levels of response. May generate alerts for officersoperating the apparatus that a person represents a threat, may put aspot on the person a target, or a quote smart flashlight quote beam thattracks the person on them, or the like permitting an officer operatingapparatus, as well as apparatus itself, to engage in more aggressiveinterdiction, arrest, or other countermeasures against the potentialassailant, or criminal.

Further referring to FIG. 15 , in some embodiments, and as a furthernon-limiting example, apparatus may use audio classification and/ordetection to determine that shots have been fired. Apparatus may useclassification to distinguish between shots fired by an officeroperating apparatus and shots fired by another person with whom officersare interacting. This may for instance be used to distinguish betweenshots fired by an officer and a shot fired by a person who is armed anddangerous. This may enable officers directing with apparatus and doorapparatus itself to respond appropriately to the situation presented. Asnoted in this disclosure, apparatus may track trajectory of a projectilebullet, a rock, a Molotov cocktail, or other missile thrown or fired bya person, so as to determine a source from which the projectile hasemanated. This may, for instance, permit apparatus to determine whatperson in a crowd has thrown and/or fired a projectile, which may enableapparatus to identify that person using a face recognition classifier,to put a spotlight on that persons for ease of detection by lawenforcement forces personnel and or other apparatus is, to identifyusing visual classifiers one or more distinctive elements of clothing orother distinguishing characteristics of that person, to enable apparatusto use targeting functionality to follow that person as they movethrough an area and or through a crowd, or the like. Apparatus and theyalso use interdiction, such as directed light to turn, directed sounddeterrent, and/or microwave pain ray against a particular persondetected to have cast a projectile fired a projectile or the like.

In some embodiments, and still referring to FIG. 15 , apparatus mayidentify particular persons according to behavioral classification, andmay transmit relevant information concerning such classification toother apparatuses, and/or to law enforcement personnel. For instance,apparatus may generate a display and or audio output that identifiesproblematic behavior, as detected using behavioral classifiers, toofficers. Likewise, where apparatus has used visual and or facerecognition classifiers to recognize a particular person, such as aknown fugitive, a person who has been reported missing, or the like,such indication may be transmitted to displayed to and are verballyoutput to an officer. In this manner, for instance, apparatus may act asa force multiplier for the function of searching for persons, forexample in the crowd or the like. Apparatus may detect persons officersare looking for when the officer himself or herself is not actuallylooking at that person. This may increase the chance that an officerwill find a person they are looking for, and increased their generalvigilance for persons who need to be found, either for the sake ofpublic safety, or for their own.

In an embodiment, and still referring to FIG. 15 , data, analysis, andother information derived apparatus via processing and/or AI may becombined and/or provided to authorized users in an augmented reality(AR) format for complete human security integration. For instance, andwithout limitation, a law enforcement officer may have an AR displaytheir person; AR display may be communicatively connected to apparatus.AR display may receive information from apparatus and/or a systemincluding apparatus. Information may be displayed to provide indicialabeling any targeted and/or identified subject and/or other itemaccording to any coordinate system described above; indicia may tracksuch targeted and/or identified subject and/or other item such thatindicia may remain on a displayed image thereof and/or of a locationwhere targeted and/or identified subject and/or other item is determinedand/or estimated to be. This may be used for aiming one or more weaponsor other devices at such subject and/or other item; alternatively oradditionally, one or more persons using AR display may be able to avoidand/or converge on a threat using indicia as a guide. Indicia mayinclude, as a non-limiting example, geo-rectified icons that conveylocation based information, messaging from other devices and/or people,current status of systems, locations of threats, friendlies, neutrals,unknowns or the like, location and/or direction of detected gunshots orother events, warnings, “keep-out” zones of countermeasures or otherdevices, or the like, while also providing two-way communication backapparatus and/or a system thereof to provide real-time status of a user,such as their location, ammo remaining, current vital signs, status,radios, or the like. The edge apparatus, networked, and/or cloudservices of a system may provide and/or coordinate to use human and/orAI analysis and planning to command and manage an integration ofautonomous, semi-autonomous, and/or human response to a situationthrough direct communication, AR, and/or other communication methods.

With further reference to FIG. 15 , AR device may be implemented in anysuitable way, including without limitation incorporation of or in a headmounted display, a head-up display, a display incorporated ineyeglasses, googles, headsets, helmet display systems, weapon sights,handheld devices, or the like, a display incorporated in contact lenses,an eye tap display system including without limitation a laser eye tapdevice, VRD, or the like. AR device may alternatively or additionally beimplemented using a projector, which may display indicia, as describedin further detail below, onto one or more images depicted on display.Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various optical projection and/or displaytechnologies that may be incorporated in augmented reality deviceconsistently with this disclosure.

Further referring to FIG. 1 , a view window, projection device, and/orother display devices incorporated in augmented reality device mayimplement a stereoscopic display. A “stereoscopic display,” as used inthis disclosure, is a display that simulates a user experience ofviewing a three-dimensional space and/or object, for instance bysimulating and/or replicating different perspectives of a user's twoeyes; this is in contrast to a two-dimensional image, in which imagespresented to each eye are substantially identical, such as may occurwhen viewing a flat screen display. Stereoscopic display may display twoflat images having different perspectives, each to only one eye, whichmay simulate the appearance of an object or space as seen from theperspective of that eye. Alternatively or additionally, stereoscopicdisplay may include a three-dimensional display such as a holographicdisplay or the like. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various alternative oradditional types of stereoscopic display that may be employed inaugmented reality device.

In some embodiments, and with continued reference to FIG. 15 , apparatusmay be configured to connect to record data concerning a given encounterbetween apparatus, law enforcement, and/or a member of the public. Forinstance, where apparatus detects shots fired, or aggressive behavior,or any other event requiring interdiction, or involving interdiction oraggressive behavior by a police officer or a member of the public,apparatus may record data, video sensor data classification data, or thelike which may be transmitted to a server, or other remote device, orstart a memory of apparatus, for later use in analysis, trial, or othersituations such as conduct investigation regarding the officer.Apparatus may aid in determining what happened in the heat of a moment,by recording data in a matter unlikely to be influenced by emotionalconcerns. In this way, apparatus may act as a dispassionate witness,ensuring that some form of objective truth may be available when factfinders need to determine what has happened in a given situation. Thismay enhance both public safety, confidence of law enforcement officersin performing their duties, and confidence of the public in the fair andequitable distribution and application of justice by persons chargedwith keeping the public peace.

In an embodiment, and still referring to FIG. 15 , apparatus may useon-board and/or communicatively connected secure, anti-tamper methods toensure data chain of custody and to make sure that data is not modifiedor damaged. These methods may be used for storing every use ofcountermeasures by apparatus, and/or any circumstances thereof, such asdata recorded of subject, subject behavior, video, imagery, sensor data,and the like. Entries may be timestamped, along with imagery, audio,energy used with countermeasure, and/or other critical data for“blackboxing” any use of countermeasures so that they may be presentedto a court and/or another reviewing body at a later time.

Continuing to refer to FIG. 15 , records may be secured, stored, and/ortransmitted according to one or more cryptographic systems. In oneembodiment, a cryptographic system is a system that converts data from afirst form, known as “plaintext,” which is intelligible when viewed inits intended format, into a second form, known as “ciphertext,” which isnot intelligible when viewed in the same way. Ciphertext may beunintelligible in any format unless first converted back to plaintext.In one embodiment, a process of converting plaintext into ciphertext isknown as “encryption.” Encryption process may involve the use of adatum, known as an “encryption key,” to alter plaintext. Cryptographicsystem may also convert ciphertext back into plaintext, which is aprocess known as “decryption.” Decryption process may involve the use ofa datum, known as a “decryption key,” to return the ciphertext to itsoriginal plaintext form. In embodiments of cryptographic systems thatare “symmetric,” decryption key is essentially the same as encryptionkey: possession of either key makes it possible to deduce the other keyquickly without further secret knowledge. Encryption and decryption keysin symmetric cryptographic systems may be kept secret and shared onlywith persons or entities that the user of the cryptographic systemwishes to be able to decrypt the ciphertext. One example of a symmetriccryptographic system is the Advanced Encryption Standard (“AES”), whicharranges plaintext into matrices and then modifies the matrices throughrepeated permutations and arithmetic operations with an encryption key.

Still referring to FIG. 15 , in embodiments of cryptographic systemsthat are “asymmetric,” either encryption or decryption key cannot bereadily deduced without additional secret knowledge, even given thepossession of a corresponding decryption or encryption key,respectively; a common example is a “public key cryptographic system,”in which possession of the encryption key does not make it practicallyfeasible to deduce the decryption key, so that the encryption key maysafely be made available to the public. An example of a public keycryptographic system is RSA, in which an encryption key involves the useof numbers that are products of very large prime numbers, but adecryption key involves the use of those very large prime numbers, suchthat deducing the decryption key from the encryption key requires thepractically infeasible task of computing the prime factors of a numberwhich is the product of two very large prime numbers. Another example iselliptic curve cryptography, which relies on the fact that given twopoints P and Q on an elliptic curve over a finite field, and adefinition for addition where A+B=−R, the point where a line connectingpoint A and point B intersects the elliptic curve, where “0,” theidentity, is a point at infinity in a projective plane containing theelliptic curve, finding a number k such that adding P to itself k timesresults in Q is computationally impractical, given correctly selectedelliptic curve, finite field, and P and Q.

In some embodiments, and with continued reference to FIG. 15 , datarecorded may be tamper-proofed by generation and storage ofcryptographic hashes thereof, also referred to by the equivalentshorthand term “hashes.” A cryptographic hash, as used herein, is amathematical representation of a lot of data, such as files or blocks ina block chain as described in further detail below; the mathematicalrepresentation is produced by a lossy “one-way” algorithm known as a“hashing algorithm.” Hashing algorithm may be a repeatable process; thatis, identical lots of data may produce identical hashes each time theyare subjected to a particular hashing algorithm. Because hashingalgorithm is a one-way function, it may be impossible to reconstruct alot of data from a hash produced from the lot of data using the hashingalgorithm. In the case of some hashing algorithms, reconstructing thefull lot of data from the corresponding hash using a partial set of datafrom the full lot of data may be possible only by repeatedly guessing atthe remaining data and repeating the hashing algorithm; it is thuscomputationally difficult if not infeasible for a single computer toproduce the lot of data, as the statistical likelihood of correctlyguessing the missing data may be extremely low. However, the statisticallikelihood of a computer of a set of computers simultaneously attemptingto guess the missing data within a useful timeframe may be higher,permitting mining protocols as described in further detail below.

In an embodiment, and still referring to FIG. 15 hashing algorithm maydemonstrate an “avalanche effect,” whereby even extremely small changesto lot of data produce drastically different hashes. This may thwartattempts to avoid the computational work necessary to recreate a hash bysimply inserting a fraudulent datum in data lot, enabling the use ofhashing algorithms for “tamper-proofing” data such as data contained inan immutable ledger as described in further detail below. This avalancheor “cascade” effect may be evinced by various hashing processes; personsskilled in the art, upon reading the entirety of this disclosure, willbe aware of various suitable hashing algorithms for purposes describedherein. Verification of a hash corresponding to a lot of data may beperformed by running the lot of data through a hashing algorithm used toproduce the hash. Such verification may be computationally expensive,albeit feasible, potentially adding up to significant processing delayswhere repeated hashing, or hashing of large quantities of data, isrequired, for instance as described in further detail below. Examples ofhashing programs include, without limitation, SHA256, a NIST standard;further current and past hashing algorithms include Winternitz hashingalgorithms, various generations of Secure Hash Algorithm (including“SHA-1,” “SHA-2,” and “SHA-3”), “Message Digest” family hashes such as“MD4,” “MD5,” “MD6,” and “RIPEMD,” Keccak, “BLAKE” hashes and progeny(e.g., “BLAKE2,” “BLAKE-256,” “BLAKE-512,” and the like), MessageAuthentication Code (“MAC”)-family hash functions such as PMAC, OMAC,VMAC, HMAC, and UMAC, Poly1305-AES, Elliptic Curve Only Hash (“ECOH”)and similar hash functions, Fast-Syndrome-based (FSB) hash functions,GOST hash functions, the Grestl hash function, the HAS-160 hashfunction, the JH hash function, the RadioGatún hash function, the Skeinhash function, the Streebog hash function, the SWIFFT hash function, theTiger hash function, the Whirlpool hash function, or any hash functionthat satisfies, at the time of implementation, the requirements that acryptographic hash be deterministic, infeasible to reverse-hash,infeasible to find collisions, and have the property that small changesto an original message to be hashed will change the resulting hash soextensively that the original hash and the new hash appear uncorrelatedto each other. A degree of security of a hash function in practice maydepend both on the hash function itself and on characteristics of themessage and/or digest used in the hash function. For example, where amessage is random, for a hash function that fulfillscollision-resistance requirements, a brute-force or “birthday attack”may to detect collision may be on the order of 0(2^(n/2)) for n outputbits; thus, it may take on the order of 2²⁵⁶ operations to locate acollision in a 512 bit output “Dictionary” attacks on hashes likely tohave been generated from a non-random original text can have a lowercomputational complexity, because the space of entries they are guessingis far smaller than the space containing all random permutations ofbits. However, the space of possible messages may be augmented byincreasing the length or potential length of a possible message, or byimplementing a protocol whereby one or more randomly selected strings orsets of data are added to the message, rendering a dictionary attacksignificantly less effective.

Continuing to refer to FIG. 15 , one or more elements of data may belabeled, secured, and/or validated using secure proofs. A “secureproof,” as used in this disclosure, is a protocol whereby an output isgenerated that demonstrates possession of a secret, such asdevice-specific secret, without demonstrating the entirety of thedevice-specific secret; in other words, a secure proof by itself, isinsufficient to reconstruct the entire device-specific secret, enablingthe production of at least another secure proof using at least adevice-specific secret. A secure proof may be referred to as a “proof ofpossession” or “proof of knowledge” of a secret. Where at least adevice-specific secret is a plurality of secrets, such as a plurality ofchallenge-response pairs, a secure proof may include an output thatreveals the entirety of one of the plurality of secrets, but not all ofthe plurality of secrets; for instance, secure proof may be a responsecontained in one challenge-response pair. In an embodiment, proof maynot be secure; in other words, proof may include a one-time revelationof at least a device-specific secret, for instance as used in a singlechallenge-response exchange.

Still referring to FIG. 15 , secure proof may include a zero-knowledgeproof, which may provide an output demonstrating possession of a secretwhile revealing none of the secret to a recipient of the output;zero-knowledge proof may be information-theoretically secure, meaningthat an entity with infinite computing power would be unable todetermine secret from output. Alternatively, zero-knowledge proof may becomputationally secure, meaning that determination of secret from outputis computationally infeasible, for instance to the same extent thatdetermination of a private key from a public key in a public keycryptographic system is computationally infeasible. Zero-knowledge proofalgorithms may generally include a set of two algorithms, a proveralgorithm, or “P,” which is used to prove computational integrity and/orpossession of a secret, and a verifier algorithm, or “V” whereby a partymay check the validity of P. Zero-knowledge proof may include aninteractive zero-knowledge proof, wherein a party verifying the proofmust directly interact with the proving party; for instance, theverifying and proving parties may be required to be online, or connectedto the same network as each other, at the same time. Interactivezero-knowledge proof may include a “proof of knowledge” proof, such as aSchnorr algorithm for proof on knowledge of a discrete logarithm. in aSchnorr algorithm, a prover commits to a randomness r, generates amessage based on r, and generates a message adding r to a challenge cmultiplied by a discrete logarithm that the prover is able to calculate;verification is performed by the verifier who produced c byexponentiation, thus checking the validity of the discrete logarithm.Interactive zero-knowledge proofs may alternatively or additionallyinclude sigma protocols. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various alternativeinteractive zero-knowledge proofs that may be implemented consistentlywith this disclosure.

Alternatively, and further referring to FIG. 15 , zero-knowledge proofmay include a non-interactive zero-knowledge, proof, or a proof whereinneither party to the proof interacts with the other party to the proof;for instance, each of a party receiving the proof and a party providingthe proof may receive a reference datum which the party providing theproof may modify or otherwise use to perform the proof. As anon-limiting example, zero-knowledge proof may include a succinctnon-interactive arguments of knowledge (ZK-SNARKS) proof, wherein a“trusted setup” process creates proof and verification keys using secret(and subsequently discarded) information encoded using a public keycryptographic system, a prover runs a proving algorithm using theproving key and secret information available to the prover, and averifier checks the proof using the verification key; public keycryptographic system may include RSA, elliptic curve cryptography,ElGamal, or any other suitable public key cryptographic system.Generation of trusted setup may be performed using a secure multipartycomputation so that no one party has control of the totality of thesecret information used in the trusted setup; as a result, if any oneparty generating the trusted setup is trustworthy, the secretinformation may be unrecoverable by malicious parties. As anothernon-limiting example, non-interactive zero-knowledge proof may include aSuccinct Transparent Arguments of Knowledge (ZK-STARKS) zero-knowledgeproof. In an embodiment, a ZK-STARKS proof includes a Merkle root of aMerkle tree representing evaluation of a secret computation at somenumber of points, which may be 1 billion points, plus Merkle branchesrepresenting evaluations at a set of randomly selected points of thenumber of points; verification may include determining that Merklebranches provided match the Merkle root, and that point verifications atthose branches represent valid values, where validity is shown bydemonstrating that all values belong to the same polynomial created bytransforming the secret computation. In an embodiment, ZK-STARKS doesnot require a trusted setup.

Continuing to refer to FIG. 15 , zero-knowledge proof may include anyother suitable zero-knowledge proof. Zero-knowledge proof may include,without limitation bulletproofs. Zero-knowledge proof may include ahomomorphic public-key cryptography (hPKC)-based proof. Zero-knowledgeproof may include a discrete logarithmic problem (DLP) proof.Zero-knowledge proof may include a secure multi-party computation (MPC)proof. Zero-knowledge proof may include, without limitation, anincrementally verifiable computation (IVC). Zero-knowledge proof mayinclude an interactive oracle proof (IOP). Zero-knowledge proof mayinclude a proof based on the probabilistically checkable proof (PCP)theorem, including a linear PCP (LPCP) proof. Persons skilled in theart, upon reviewing the entirety of this disclosure, will be aware ofvarious forms of zero-knowledge proofs that may be used, singly or incombination, consistently with this disclosure.

In an embodiment, and still referring to FIG. 15 , secure proof may beimplemented using a challenge-response protocol. In an embodiment, thismay function as a one-time pad implementation; for instance, amanufacturer or other trusted party may record a series of outputs(“responses”) produced by a device possessing secret information, givena series of corresponding inputs (“challenges”), and store themsecurely. In an embodiment, a challenge-response protocol may becombined with key generation. A single key may be used in one or moredigital signatures as described in further detail below, such assignatures used to receive and/or transfer possession of crypto-currencyassets; the key may be discarded for future use after a set period oftime. In an embodiment, varied inputs include variations in localphysical parameters, such as fluctuations in local electromagneticfields, radiation, temperature, and the like, such that an almostlimitless variety of private keys may be so generated. Secure proof mayinclude encryption of a challenge to produce the response, indicatingpossession of a secret key. Encryption may be performed using a privatekey of a public key cryptographic system, or using a private key of asymmetric cryptographic system; for instance, trusted party may verifyresponse by decrypting an encryption of challenge or of another datumusing either a symmetric or public-key cryptographic system, verifyingthat a stored key matches the key used for encryption as a function ofat least a device-specific secret. Keys may be generated by randomvariation in selection of prime numbers, for instance for the purposesof a cryptographic system such as RSA that relies prime factoringdifficulty. Keys may be generated by randomized selection of parametersfor a seed in a cryptographic system, such as elliptic curvecryptography, which is generated from a seed. Keys may be used togenerate exponents for a cryptographic system such as Diffie-Helman orElGamal that are based on the discrete logarithm problem.

Still referring to FIG. 15 , one or more elements of data, cryptographichashes, or the like, may be signed and/or stamped using a digitalsignature. A “digital signature,” as used herein, includes a secureproof of possession of a secret by a signing device, as performed onprovided element of data, known as a “message.” A message may include anencrypted mathematical representation of a file or other set of datausing the private key of a public key cryptographic system. Secure proofmay include any form of secure proof as described above, includingwithout limitation encryption using a private key of a public keycryptographic system as described above. Signature may be verified usinga verification datum suitable for verification of a secure proof, forinstance, where secure proof is enacted by encrypting message using aprivate key of a public key cryptographic system, verification mayinclude decrypting the encrypted message using the corresponding publickey and comparing the decrypted representation to a purported match thatwas not encrypted; if the signature protocol is well-designed andimplemented correctly, this means the ability to create the digitalsignature is equivalent to possession of the private decryption keyand/or device-specific secret. Likewise, if a message making up amathematical representation of file is well-designed and implementedcorrectly, any alteration of the file may result in a mismatch with thedigital signature; the mathematical representation may be produced usingan alteration-sensitive, reliably reproducible algorithm, such as ahashing algorithm as described above. A mathematical representation towhich the signature may be compared may be included with signature, forverification purposes; in other embodiments, the algorithm used toproduce the mathematical representation may be publicly available,permitting the easy reproduction of the mathematical representationcorresponding to any file.

Still viewing FIG. 15 , in some embodiments, digital signatures may becombined with or incorporated in digital certificates. In oneembodiment, a digital certificate is a file that conveys information andlinks the conveyed information to a “certificate authority” that is theissuer of a public key in a public key cryptographic system. Certificateauthority in some embodiments contains data conveying the certificateauthority's authorization for the recipient to perform a task. Theauthorization may be the authorization to access a given datum. Theauthorization may be the authorization to access a given process. Insome embodiments, the certificate may identify the certificateauthority. The digital certificate may include a digital signature.

With continued reference to FIG. 15 , in some embodiments, a third partysuch as a certificate authority (CA) is available to verify that thepossessor of the private key is a particular entity; thus, if thecertificate authority may be trusted, and the private key has not beenstolen, the ability of an entity to produce a digital signature confirmsthe identity of the entity and links the file to the entity in averifiable way. Digital signature may be incorporated in a digitalcertificate, which is a document authenticating the entity possessingthe private key by authority of the issuing certificate authority andsigned with a digital signature created with that private key and amathematical representation of the remainder of the certificate. Inother embodiments, digital signature is verified by comparing thedigital signature to one known to have been created by the entity thatpurportedly signed the digital signature; for instance, if the publickey that decrypts the known signature also decrypts the digitalsignature, the digital signature may be considered verified. Digitalsignature may also be used to verify that the file has not been alteredsince the formation of the digital signature.

Still referring to FIG. 15 , anti-tamper functions may use of encryptionof data to obscure its contents and/or meaning; encrypted data may betimestamped but otherwise indecipherable until decrypted, ensuring thatpersons who are not authorized to view the data do not have an abilityto determine what the data is. Timestamps may further be encrypted, andrecords placed in an arbitrary and/or non-chronological order, such thatdetermination of a time relating to a particular record is contingent ondecryption of a timestamp thereof.

Still referring to FIG. 15 , apparatus and/or devices connected theretomay use full disk encryption to enforce encryption and preventinformation leaks resulting from theft or loss. Full disc encryption mayinclude automatic encryption of an entire hard disk and/or memorystorage device, including an operating system (OS) of apparatus or otherdevice, booting the encrypted OS, and prevention of a removed hard diskfrom being decrypted. For the first element, an encryption function maybe built into a device driver layer of apparatus or other device, forinstance as a filter driver. This may mean that an encryption functionoperates as part of an OS, and all write access to a hard disk or othermemory system by application programs or the OS is encryptedautomatically. Also, because it runs in the same layer as OS, encryptionfunction may not affect behavior of application programs or other useroperations. The second element described above may be accomplished usingpreboot authentication that performs authentication before the OS boots.The third element may be dealt with by using a proprietary boot loaderto decrypt the OS as it boots.

In an embodiment, and continuing to refer to FIG. 15 , full encryptionmay enforce encryption of all data on a hard disk, including filesoutput by the OS. One advantage of this function may be informationleaks from occurring through inattention, such as a user forgetting toencrypt data. Another advantage is that even files output by OS areencrypted. Although users may not be generally aware of files output byan OS, there have been cases when information has been extracted fromthese files. Paging files are one way this can happen. Paging files maybe used by an OS to cache data temporarily when memory hardware capacityis insufficient. Such files may be a source of leaks because they maycontain data from volatile memory unencrypted without a user knowing.While a simple file encryption function may not perform encryptions forthese special files, a full disk encryption may, thereby preventingtheft of information they contain.

Still referring to FIG. 15 , reboot authentication may include afunction added along with a full disk encryption function to performuser authentication prior to an OS booting. As decrypting of hard diskdata cannot start until after this authentication is successful, anunauthorized user may be unable to read its contents even if they removea hard disk and attach it to another PC. This function may provide PCswith a very robust authentication mechanism because it may use its ownauthentication method, making it separate to any other authenticationsuch as that performed by a BIOS or OS.

Continuing to refer to FIG. 15 , communications encryption may preventleaks of information from communications data by encrypting allcommunications. Such encryption may ensure that the communications datafrom all application programs running on an OS is encryptedautomatically. To achieve this, communications encryption may beimplemented in a device driver layer, similarly to a full diskencryption function. This may ensure that communications data from allapplication programs is encrypted automatically, and there is no effecton the behavior of the application programs or user operation. Thisfunction may be provided for the following two types of communications,depending on the application: Communications via encrypted virtual hubmay include a method that encrypts Ethernet frames and uses TransmissionControl Protocol/Internet Protocol (TCP/IP) encapsulation. This methodmay be used for multipoint-to-multipoint communications. Also,authenticating each communications packet may prevent tampering with orspoofing of communications data, and communications can use proprietarycryptographic algorithms to achieve high levels of security, asrequired. Another feature of this method is that each device maycommunicate via a server called an “encrypted virtual hub.” Encryptedvirtual hubs, as used in this disclosure, are servers that emulate aphysical hub. Encrypted virtual hubs may be used to run a single virtualnetwork on top of a WAN (called an “overlay network”) so that devicesthat connect to the encrypted virtual hubs can communicate as if theyare all on the same local area network (LAN).

Still referring to FIG. 15 , multipoint-to-multipoint communications maywork according to similar principles to those described above. A featureof this communications mechanism may include that, since thesecommunication traffic streams are encapsulated as TCP/IP packets (suchas port No. 80: Hypertext Transfer Protocol packets), each peer in anetwork may communicate even over firewalls or Network AddressTranslation (NATs). For this reason, this way of communication may havean advantage when a user cannot choose an ideal network environment.IPsec transport mode communications Security Architecture for InternetProtocol (IPsec) transport mode communications may mean that IPseccommunications in which only the Transmission Control Protocol/UserDatagram Protocol (TCP/UDP) payload is encrypted. Like encrypted virtualhub communications, multipoint-to-multipoint communications may useproprietary cryptographic algorithms and perform authentication for eachcommunications packet. Being simpler than encrypted virtual hubcommunications, high throughput may be an advantage. This may make itsuitable for use on communication links that can be configured asrequired, such as between headquarters and branch offices, for example.

Still referring to FIG. 15 , apparatus 100 and/or system may preventtampering with data and/or code using one or more hardware-basedprotections. For instance, apparatus 100 and/or system may use a trustedprocessor. Trusted, tamper-resistant hardware may check and verify everypiece of hardware and software that exists—or that requests to be run ona computer such as in apparatus 100 and/or system—starting at a boot-upprocess. Hardware may guarantee integrity by checking every entity whenapparatus 100 and/or system boots up, and every entity that may be runor used on apparatus 100 and/or system after it boots up. Hardware may,for example, store all keys necessary to verify digital signatures,decrypt licenses, decrypt software before running it, and encryptmessages during any online protocols it may need to run (e.g., forupdates) with another trusted remote entity (such as a softwarepublisher). Software downloaded onto apparatus 100 and/or system may bestored in encrypted form on hard drive and may be decrypted and executedby secure hardware, which may also encrypt and decrypt information itsends and receives from its random-access memory. Similar or the samesoftware, data, and/or media may be encrypted in a different way foreach trusted processor that may execute it because each processor mayhave a distinctive decryption key. Alternatively or additionallyhardware such as a smart card or physically secure token may beemployed; such lightweight hardware protection techniques may requirethat such hardware be present for software to run, to have certainfunctionality, to access a media file, or the like.

Further referring to FIG. 15 , apparatus 100 and/or system may employencryption wrapper software security. With encryption wrapper softwaresecurity, critical portions of software, or possibly all of it, may beencrypted and/or decrypted dynamically at run-time. In some embodiments,at no time during execution is the whole software in the clear; code maydecrypt just before it executes, leaving other parts of the programstill encrypted. Therefore, no single snapshot of memory may expose anentire decrypted program. Encryption wrappers often may use lightweightencryption to minimize computational cost of executing a protectedprogram. Encryption may be advantageously combined with compression:This may result in a smaller amount of storage usage, and also may makeencryption harder to defeat by cryptanalysis.

Still referring to FIG. 15 , apparatus 100 and/or system may employsoftware and/or data watermarking and fingerprinting the goal ofwatermarking may be to embed information into software and/or data in amanner that makes it hard to remove by an adversary without damagingsoftware functionality and/or data integrity. Information inserted mayinclude purchaser information, or it may include an integrity check todetect modification, the placing of caption-type information, etc. Awatermark need not be stealthy; visible watermarks may act as adeterrent (against piracy, for example). In steganography (the art ofconcealing the existence of information within seemingly innocuouscarriers), a mark may be required to be stealthy: its very existence maynot be detectable. A specific type of watermarking is fingerprinting,which embeds a unique message in each instance of the software fortraitor tracing. This may have consequences for an adversary's abilityto attack a watermark: two differently marked copies often make possiblea diff attack that compares the two differently marked copies and canenable the adversary to create a usable copy that has neither one of thetwo marks. Thus, in any fingerprinting scheme, may be useful to usetechniques that are resilient against such comparison attacks. Awatermark may be robust (hard to remove). In some situations, however, afragile watermark may be desirable; it is destroyed if even a smallalteration is made to the software (e.g., this is useful for making thesoftware tamper-evident). Software watermarks may be static, i.e.,readable without running the software, or could appear only at run-time(preferably in an evanescent form). In either case, reading a watermarkusually may require knowing a secret key, without which the watermarkremains invisible.

Still referring to 15, apparatus 100 and/or system may perform tamperproofing by employing a guard. In an embodiment, a guard may includecode that is injected into software for the sake of AT protection. Aguard may not interfere with a program's basic functionality unless thatprogram is tampered with—it is tampering that may trigger a guard totake action that deviates from normal program behavior. Examples ofguard functionality range from tasks as simple as comparing a checksumof a code fragment to its expected value, to repairing code (in case itwas maliciously damaged), to complex and indirect forms of protectionthrough subtle side effects. Guarding may include injecting into code tobe protected a large number of guards that mutually protect each otheras well as a software program in which they now reside. Guards may alsobe used to good effect in conjunction with hardware-based protectiontechniques to further ensure that protected software is only executed inan authorized environment. Numbers, types, and stealthiness of guards;protection topology (who protects who); and where the guards areinjected in the original code and how they are entangled with it aresome of the parameters in the strength of the resulting protection: Allsuch parameters may be tunable in a manner that depends on a type ofcode being protected, a desired level of protection, or the like. Suchprotection may be performed and/or inserted in a highly automatedfashion using high-level scripts that specify the protection guidelinesand parameters. It should be thought of as a part of the compilationprocess where an anti-tamper option results in code that is guarded andtamper resistant. A guard's response when it detects tampering mayflexible and may range from a mild response to disruption of normalprogram execution through injection of run-time errors (crashes or evensubtle errors in the answers computed); a reaction chosen may depend onthe software publisher's business model and the expected adversary.

Still referring to FIG. 15 , one or more elements of data may betamper-proofed via including in an immutable sequential listing. An“immutable sequential listing,” as used in this disclosure, is a datastructure that places data entries in a fixed sequential arrangement,such as a temporal sequence of entries and/or blocks thereof, where thesequential arrangement, once established, cannot be altered orreordered. An immutable sequential listing may be, include and/orimplement an immutable ledger, where data entries that have been postedto the immutable sequential listing cannot be altered.

Referring now to FIG. 16 , an exemplary embodiment of an immutablesequential listing 1600 is illustrated. Data elements are listing inimmutable sequential listing 1600; data elements may include any form ofdata, including textual data, image data, encrypted data,cryptographically hashed data, and the like. Data elements may include,without limitation, one or more at least a digitally signed assertions.In one embodiment, a digitally signed assertion 1604 is a collection oftextual data signed using a secure proof as described in further detailbelow; secure proof may include, without limitation, a digital signatureas described above. Collection of textual data may contain any textualdata, including without limitation American Standard Code forInformation Interchange (ASCII), Unicode, or similar computer-encodedtextual data, any alphanumeric data, punctuation, diacritical mark, orany character or other marking used in any writing system to conveyinformation, in any form, including any plaintext or cyphertext data; inan embodiment, collection of textual data may be encrypted, or may be ahash of other data, such as a root or node of a Merkle tree or hashtree, or a hash of any other information desired to be recorded in somefashion using a digitally signed assertion 1604. In an embodiment,collection of textual data states that the owner of a certaintransferable item represented in a digitally signed assertion 1604register is transferring that item to the owner of an address. Adigitally signed assertion 1604 may be signed by a digital signaturecreated using the private key associated with the owner's public key, asdescribed above.

Still referring to FIG. 16 , a digitally signed assertion 1604 maydescribe a transfer of virtual currency, such as crypto currency asdescribed below. The virtual currency may be a digital currency. Item ofvalue may be a transfer of trust, for instance represented by astatement vouching for the identity or trustworthiness of the firstentity. Item of value may be an interest in a fungible negotiablefinancial instrument representing ownership in a public or privatecorporation, a creditor relationship with a governmental body or acorporation, rights to ownership represented by an option, derivativefinancial instrument, commodity, debt-backed security such as a bond ordebenture or other security as described in further detail below. Aresource may be a physical machine e.g. a ride share vehicle or anyother asset. A digitally signed assertion 1604 may describe the transferof a physical good; for instance, a digitally signed assertion 1604 maydescribe the sale of a product. In some embodiments, a transfernominally of one item may be used to represent a transfer of anotheritem; for instance, a transfer of virtual currency may be interpreted asrepresenting a transfer of an access right; conversely, where the itemnominally transferred is something other than virtual currency, thetransfer itself may still be treated as a transfer of virtual currency,having value that depends on many potential factors including the valueof the item nominally transferred and the monetary value attendant tohaving the output of the transfer moved into a particular user'scontrol. The item of value may be associated with a digitally signedassertion 1604 by means of an exterior protocol, such as the COLOREDCOINS created according to protocols developed by The Colored CoinsFoundation, the MASTERCOIN protocol developed by the MastercoinFoundation, or the ETHEREUM platform offered by the Stiftung EthereumFoundation of Baar, Switzerland, the Thunder protocol developed byThunder Consensus, or any other protocol.

Still referring to FIG. 16 , in one embodiment, an address is a textualdatum identifying the recipient of virtual currency or another item ofvalue in a digitally signed assertion 1604. In some embodiments, addressis linked to a public key, the corresponding private key of which isowned by the recipient of a digitally signed assertion 1604. Forinstance, address may be the public key. Address may be arepresentation, such as a hash, of the public key. Address may be linkedto the public key in memory of a computing device, for instance via a“wallet shortener” protocol. Where address is linked to a public key, atransferee in a digitally signed assertion 1604 may record a subsequenta digitally signed assertion 1604 transferring some or all of the valuetransferred in the first a digitally signed assertion 1604 to a newaddress in the same manner. A digitally signed assertion 1604 maycontain textual information that is not a transfer of some item of valuein addition to, or as an alternative to, such a transfer. For instance,as described in further detail below, a digitally signed assertion 1604may indicate a confidence level associated with a distributed storagenode as described in further detail below.

In an embodiment, and still referring to FIG. 16 immutable sequentiallisting 1600 records a series of at least a posted content in a way thatpreserves the order in which the at least a posted content took place.Temporally sequential listing may be accessible at any of varioussecurity settings; for instance, and without limitation, temporallysequential listing may be readable and modifiable publicly, may bepublicly readable but writable only by entities and/or devices havingaccess privileges established by password protection, confidence level,or any device authentication procedure or facilities described herein,or may be readable and/or writable only by entities and/or deviceshaving such access privileges. Access privileges may exist in more thanone level, including, without limitation, a first access level orcommunity of permitted entities and/or devices having ability to read,and a second access level or community of permitted entities and/ordevices having ability to write; first and second community may beoverlapping or non-overlapping. In an embodiment, posted content and/orimmutable sequential listing 1600 may be stored as one or more zeroknowledge sets (ZKS), Private Information Retrieval (PIR) structure, orany other structure that allows checking of membership in a set byquerying with specific properties. Such database may incorporateprotective measures to ensure that malicious actors may not query thedatabase repeatedly in an effort to narrow the members of a set toreveal uniquely identifying information of a given posted content.

Still referring to FIG. 16 , immutable sequential listing 1600 maypreserve the order in which the at least a posted content took place bylisting them in chronological order; alternatively or additionally,immutable sequential listing 1600 may organize digitally signedassertions 1604 into sub-listings 1608 such as “blocks” in a blockchain,which may be themselves collected in a temporally sequential order;digitally signed assertions 1604 within a sub-listing 1608 may or maynot be temporally sequential. The ledger may preserve the order in whichat least a posted content took place by listing them in sub-listings1608 and placing the sub-listings 1608 in chronological order. Theimmutable sequential listing 1600 may be a distributed, consensus-basedledger, such as those operated according to the protocols promulgated byRipple Labs, Inc., of San Francisco, Calif., or the Stellar DevelopmentFoundation, of San Francisco, Calif, or of Thunder Consensus. In someembodiments, the ledger is a secured ledger; in one embodiment, asecured ledger is a ledger having safeguards against alteration byunauthorized parties. The ledger may be maintained by a proprietor, suchas a system administrator on a server, that controls access to theledger; for instance, the user account controls may allow contributorsto the ledger to add at least a posted content to the ledger, but maynot allow any users to alter at least a posted content that have beenadded to the ledger. In some embodiments, ledger is cryptographicallysecured; in one embodiment, a ledger is cryptographically secured whereeach link in the chain contains encrypted or hashed information thatmakes it practically infeasible to alter the ledger without betrayingthat alteration has taken place, for instance by requiring that anadministrator or other party sign new additions to the chain with adigital signature. Immutable sequential listing 1600 may be incorporatedin, stored in, or incorporate, any suitable data structure, includingwithout limitation any database, datastore, file structure, distributedhash table, directed acyclic graph or the like. In some embodiments, thetimestamp of an entry is cryptographically secured and validated viatrusted time, either directly on the chain or indirectly by utilizing aseparate chain. In one embodiment the validity of timestamp is providedusing a time stamping authority as described in the RFC 3161 standardfor trusted timestamps, or in the ANSI ASC x9.95 standard. In anotherembodiment, the trusted time ordering is provided by a group of entitiescollectively acting as the time stamping authority with a requirementthat a threshold number of the group of authorities sign the timestamp.

In some embodiments, and with continued reference to FIG. 16 , immutablesequential listing 1600, once formed, may be inalterable by any party,no matter what access rights that party possesses. For instance,immutable sequential listing 1600 may include a hash chain, in whichdata is added during a successive hashing process to ensurenon-repudiation. Immutable sequential listing 1600 may include a blockchain. In one embodiment, a block chain is immutable sequential listing1600 that records one or more new at least a posted content in a dataitem known as a sub-listing 1608 or “block.” An example of a block chainis the BITCOIN block chain used to record BITCOIN transactions andvalues. Sub-listings 1608 may be created in a way that places thesub-listings 1608 in chronological order and link each sub-listing 1608to a previous sub-listing 1608 in the chronological order so that anycomputing device may traverse the sub-listings 1608 in reversechronological order to verify any at least a posted content listed inthe block chain. Each new sub-listing 1608 may be required to contain acryptographic hash describing the previous sub-listing 1608. In someembodiments, the block chain contains a single first sub-listing 1608sometimes known as a “genesis block.”

Still referring to FIG. 16 , the creation of a new sub-listing 1608 maybe computationally expensive; for instance, the creation of a newsub-listing 1608 may be designed by a “proof of work” protocol acceptedby all participants in forming the immutable sequential listing 1600 totake a powerful set of computing devices a certain period of time toproduce. Where one sub-listing 1608 takes less time for a given set ofcomputing devices to produce the sub-listing 1608 protocol may adjustthe algorithm to produce the next sub-listing 1608 so that it willrequire more steps; where one sub-listing 1608 takes more time for agiven set of computing devices to produce the sub-listing 1608 protocolmay adjust the algorithm to produce the next sub-listing 1608 so that itwill require fewer steps. As an example, protocol may require a newsub-listing 1608 to contain a cryptographic hash describing itscontents; the cryptographic hash may be required to satisfy amathematical condition, achieved by having the sub-listing 1608 containa number, called a nonce, whose value is determined after the fact bythe discovery of the hash that satisfies the mathematical condition.Continuing the example, the protocol may be able to adjust themathematical condition so that the discovery of the hash describing asub-listing 1608 and satisfying the mathematical condition requires moreor less steps, depending on the outcome of the previous hashing attempt.

Mathematical condition, as an example, might be that the hash contains acertain number of leading zeros and a hashing algorithm that requiresmore steps to find a hash containing a greater number of leading zeros,and fewer steps to find a hash containing a lesser number of leadingzeros. In some embodiments, production of a new sub-listing 1608according to the protocol is known as “mining.” The creation of a newsub-listing 1608 may be designed by a “proof of stake” protocol as willbe apparent to those skilled in the art upon reviewing the entirety ofthis disclosure.

Continuing to refer to FIG. 16 , in some embodiments, protocol alsocreates an incentive to mine new sub-listings 1608. The incentive may befinancial; for instance, successfully mining a new sub-listing 1608 mayresult in the person or entity that mines the sub-listing 1608 receivinga predetermined amount of currency. The currency may be fiat currency.Currency may be cryptocurrency as defined below. In other embodiments,incentive may be redeemed for particular products or services; theincentive may be a gift certificate with a particular business, forinstance. In some embodiments, incentive is sufficiently attractive tocause participants to compete for the incentive by trying to race eachother to the creation of sub-listings 1608 Each sub-listing 1608 createdin immutable sequential listing 1600 may contain a record or at least aposted content describing one or more addresses that receive anincentive, such as virtual currency, as the result of successfullymining the sub-listing 1608.

With continued reference to FIG. 16 , where two entities simultaneouslycreate new sub-listings 1608, immutable sequential listing 1600 maydevelop a fork; protocol may determine which of the two alternatebranches in the fork is the valid new portion of the immutablesequential listing 1600 by evaluating, after a certain amount of timehas passed, which branch is longer. “Length” may be measured accordingto the number of sub-listings 1608 in the branch. Length may be measuredaccording to the total computational cost of producing the branch.Protocol may treat only at least a posted content contained the validbranch as valid at least a posted content. When a branch is foundinvalid according to this protocol, at least a posted content registeredin that branch may be recreated in a new sub-listing 1608 in the validbranch; the protocol may reject “double spending” at least a postedcontent that transfer the same virtual currency that another at least aposted content in the valid branch has already transferred. As a result,in some embodiments the creation of fraudulent at least a posted contentrequires the creation of a longer immutable sequential listing 1600branch by the entity attempting the fraudulent at least a posted contentthan the branch being produced by the rest of the participants; as longas the entity creating the fraudulent at least a posted content islikely the only one with the incentive to create the branch containingthe fraudulent at least a posted content, the computational cost of thecreation of that branch may be practically infeasible, guaranteeing thevalidity of all at least a posted content in the immutable sequentiallisting 1600.

Still referring to FIG. 16 , additional data linked to at least a postedcontent may be incorporated in sub-listings 1608 in the immutablesequential listing 1600; for instance, data may be incorporated in oneor more fields recognized by block chain protocols that permit a personor computer forming a at least a posted content to insert additionaldata in the immutable sequential listing 1600. In some embodiments,additional data is incorporated in an unspendable at least a postedcontent field. For instance, the data may be incorporated in anOP_RETURN within the BITCOIN block chain. In other embodiments,additional data is incorporated in one signature of a multi-signature atleast a posted content. In an embodiment, a multi-signature at least aposted content is at least a posted content to two or more addresses. Insome embodiments, the two or more addresses are hashed together to forma single address, which is signed in the digital signature of the atleast a posted content. In other embodiments, the two or more addressesare concatenated. In some embodiments, two or more addresses may becombined by a more complicated process, such as the creation of a Merkletree or the like. In some embodiments, one or more addressesincorporated in the multi-signature at least a posted content aretypical crypto-currency addresses, such as addresses linked to publickeys as described above, while one or more additional addresses in themulti-signature at least a posted content contain additional datarelated to the at least a posted content; for instance, the additionaldata may indicate the purpose of the at least a posted content, asidefrom an exchange of virtual currency, such as the item for which thevirtual currency was exchanged. In some embodiments, additionalinformation may include network statistics for a given node of network,such as a distributed storage node, e.g. the latencies to nearestneighbors in a network graph, the identities or identifying informationof neighboring nodes in the network graph, the trust level and/ormechanisms of trust (e.g. certificates of physical encryption keys,certificates of software encryption keys, (in non-limiting examplecertificates of software encryption may indicate the firmware version,manufacturer, hardware version and the like), certificates from atrusted third party, certificates from a decentralized anonymousauthentication procedure, and other information quantifying the trustedstatus of the distributed storage node) of neighboring nodes in thenetwork graph, IP addresses, GPS coordinates, and other informationinforming location of the node and/or neighboring nodes, geographicallyand/or within the network graph. In some embodiments, additionalinformation may include history and/or statistics of neighboring nodeswith which the node has interacted. In some embodiments, this additionalinformation may be encoded directly, via a hash, hash tree or otherencoding.

With continued reference to FIG. 16 , in some embodiments, virtualcurrency is traded as a crypto currency. In one embodiment, a cryptocurrency is a digital, currency such as Bitcoins, Peercoins, Namecoins,and Litecoins. Crypto-currency may be a clone of anothercrypto-currency. The crypto-currency may be an “alt-coin.”Crypto-currency may be decentralized, with no particular entitycontrolling it; the integrity of the crypto-currency may be maintainedby adherence by its participants to established protocols for exchangeand for production of new currency, which may be enforced by softwareimplementing the crypto-currency. Crypto currency may be centralized,with its protocols enforced or hosted by a particular entity. Forinstance, crypto currency may be maintained in a centralized ledger, asin the case of the XRP currency of Ripple Labs, Inc., of San Francisco,Calif. In lieu of a centrally controlling authority, such as a nationalbank, to manage currency values, the number of units of a particularcrypto-currency may be limited; the rate at which units ofcrypto-currency enter the market may be managed by a mutuallyagreed-upon process, such as creating new units of currency whenmathematical puzzles are solved, the degree of difficulty of the puzzlesbeing adjustable to control the rate at which new units enter themarket. Mathematical puzzles may be the same as the algorithms used tomake productions of sub-listings 1608 in a block chain computationallychallenging; the incentive for producing sub-listings 1608 may includethe grant of new crypto currency to the miners. Quantities of cryptocurrency may be exchanged using at least a posted content as describedabove.

In an embodiment, and still referring to FIG. 16 , when networked,system and/or apparatus may use an immutable sequential listing such asa blockchain or derivative thereof, to store a ledger of data so it canbe especially secure and/or tamper evident. In an embodiment, thisapproach may enable secure backup and non-repudiation of the data; itmay also not allow people to manipulate it such as in a court case if aplaintiff asks for data to be manipulated—defendants may check the dataagainst a public ledger and see it was manipulated. In embodiments,there may be an ability to share data recorded by system anonymously ifdesired; for instance, a neighborhood with several systems present atdifferent households may agree to share a face of a person that tried tobreak into one of the houses, a license plate of a car, or the like.System may use appropriate data scrubbing techniques to remove privateand identifying information from the data so it can be shared to theother systems for use; for instance, recipients may not know who sentit, or where the incident took place, exactly, but may know there is athreat that needs shared.

In embodiments, and referring again to FIG. 15 , a first apparatus maybe mounted to a ventral surface of a person while may be mounteddorsally; these devices may continuously scan using any or alltechniques described above to detect threatening behavior, and mayautomatically engage deterrents to protect a person, such as a policeofficer, wearing apparatuses. A cordon of officers with mounted devicesmay be able to mesh devices together, permitting coordination ofdeterrents as described herein; this may be used for crowd-control,deterrence of multiple persons simultaneously, or the like.

Still referring to FIG. 15 , an apparatus may be integrated into a scopeon the end of a gun; in an embodiment the first thing fired may be adeterrent such as a laser into subject's eyes. Alternatively, in thecase of multiple assailants, deterrent could engage persons not incurrent line of fire of the gun, reducing their ability to combat thegun holder. Deterrent and/or a speaker system may be designed to gounder and/or on top of a police car. For instance, a laser may bemounted top with a 360-degree rotation. Persons aggressively approachingcar may be interdicted using any deterrent output as described herein.An apparatus mounted on a car or shoulder may be able to shine directedlight deterrent through a rear-view mirror and/or window onto a face ofa user; aiming may be guided using retroreflection. In an embodiment,apparatus 100 may use tracking functions as described above to keep ascope spot or other light on a subject. Apparatus 100, with one or moresensing components and one or more light sources, may include a handheldand/or head or body-mounted device that uses targeting functions asdescribed above to keep a beam and/or spot of light such as a scope spotor a flashlight beam trained on a subject; apparatus 100 may function asa “smart flashlight” that uses tracking to keep an object and/or personof interest illuminated. In an embodiment, a user may activate trackingof object of interest by illuminating object and pressing a button,making a voice command, or otherwise entering a command to apparatus 100to track an object currently being illuminated, which apparatus maydetect using any light sensors and/or imaging devices as describedabove.

In an embodiment, apparatus 100 or a plurality of apparatuses may beused to provide messaging to vehicles and/or direct vehicles and/ortraffic. For instance, and without limitation, a laser or other devicemay be used to draw and/or project messages on vehicle windows and/orinterior surfaces; drawing may include lettering and/or indicatorsproviding instructions such as directional instructions, warnings abouttraffic and/or weather conditions, detours, warnings of detours and/orconstruction, and/or simulated road signs. Apparatus and/or apparatusesmay use indications projected onto roads or other driving surfaces tomake temporary lane markers, any instructions as described above, or thelike. Light curtains may be generated to demarcate lanes of permitted orsuggested travel and/or to indicate desired traffic directions.

In an embodiment, and still referring to FIG. 15 , apparatus may bedeployed to perform traffic regulation, guidance, and or interdiction.Apparatus may identify individual drivers, individual cars, licenseplates, and or behaviors in which drivers are behaving. For instanceapparatus may use image classifiers and or behavioral classifiers todetermine that a person driving a particular make or model of car andhaving a particular license plate number is driving in an erraticmanner, such as a manner indicative of inebriation, distraction,aggression, or the like apparatus may identify one or more problematicor illegal traffic behaviors, such as tailgating, “brake-checking”,speeding, changing lanes without signaling, failure to yield in apassing lane, add or gestures or behaviors indicative of aggression,among many other examples that may occur to person skilled in the artupon reading the entirety of this disclosure. Apparatus may identifypersons within cars, for instance using image classification.Alternatively, apparatus may associate license plate numbers or otherindicia identifying particular cars with owners thereof. To performtraffic analysis detection or interdiction, apparatus may be mounted atthe side of the road, in a traffic light, on highway signs or streetsigns, or in any other position. Apparatus may be mounted on the drone,which may fly above traffic detecting and or analyzing driver behavior,so as to detect problems before they arise. Apparatus may be deployedwith a number of other apparatus, for instance in the form of a net meshnetwork, permitting coordinated responses along a length of road orhighway. Apparatus may further be connected via network connections orany other suitable communications link to one or more remote devices,such as devices operated by weather services, then vice is operated bytraffic authorities, or the like such a remote devices may provide twoone or more instances of apparatus information describing a currentstate of construction, information indicating current weatherconditions, information indicating current traffic position conditions,such as traffic density locations of backups, or locations of crashesthat may be causing delays.

Still referring to FIG. 15 , where apparatus is performing trafficanalysis, guidance, and/or interdiction, apparatus and/or a network ofapparatus may perform one or more actions to inform, warn, command,and/or interdict drivers. For instance, and without limitation,apparatus may draw and or right on road surfaces car windows and orrearview mirrors, interior surfaces of cars, or the like. Drawings mayinclude one or more indicators, verbal messages, or the like. Forinstance, verbal messages may indicate to a driver that the drivershould be slowing down, should try to merge into one or other lanes, orshould be prepared more delays ahead, among other examples. Messages mayalso inform a driver of icy or flooded road conditions, or of otherhazards that may be encountered ahead. Alternatively, output may be usedto interdict, and/or warn a driver. For instance, a driver that ismoving too fast may receive an output such as a spot of light indicatingthat the driver is moving too quickly, which may be color coded, or thelike; light output by directed light deterrent may be output at a levelof intensity that prevents glare and/or other kinds of visualimpairment, while working within a range of intensities that permitescalation in attempts to draw the attention of the driver. Forinstance, apparatus may initially flash a light in a given color at thedriver, in the driver's rear through window, or otherwise. Light may bedirected off axis of the visual axis of the driver so as to avoidcontacting a fovea or macula of the driver. And initial flash of lightmay be used to draw driver's attention. If driver does not look at theflash of light and/or continues to engage in an action that the flash oflight is intended to warn the driver about the flashes of light may berepeated at a higher intensity, add greater frequency, with a moreaggressive color such as a red color indicating more stringent need forattention, or the like.

Color coded and/or peripheral uses of lasers and/or other light sourcesmay be used in peripheral vision of intended targets and/or persons toconvey information to them; for instance if they see a green light, auser may know they are going at a speed limit, yellow may mean cautionor that they are beginning to exceed the speed limit, and red may meanthey are over the speed limit. This would purposely not be directed intotheir eyes using data from the scene to determine where to aim thelaser. Such methods may be used to convey information quickly to theintended target. Similar color-coding and/or peripheral vision-basedcommunication may alternatively or additionally be used to conveyinformation in contexts besides traffic.

Still referring to FIG. 1 , apparatus may first capture a driver'sattention, and follow such an action with one or more indicators and ourmessages informing the driver of ways in which the driver should modifybehavior, trajectory, or the like to avoid hazards and comply withregulations. Output may alternatively or additionally be in the form ofguidance informing drivers which way to follow for detours, to avoidconstruction, and or to avoid hazardous road conditions. Messages mayinform drivers of a location of a particular patch of black ice, forinstance, which may be detected using a behavior classifier that detectscars slipping or skidding on black ice as they drive through thatregion. Cars may be warned in advance of an upcoming area, and advisedto reduce lead reduce speed or otherwise prepare themselves to drive onthe relatively hazardous patch of road. Similar approaches may be usedfor high wind conditions, flash flooding trauma or other hazards of theroad. Apparatus, for instance in coordination with multiple apparatus,may use light curtains, and or indicia as described above, to indicatewhich way drivers should go. Apparatus may use directed light deterrentand/or other light output devices to paint temporary lane markers,indicators of a directional or other nature, or the like down on roadsin front of cars, or beside cars, so that traffic maybe safely andeffectively guided along different routes from the usual while avoidinghazardous conditions. Apparatus may interactively inform drivers of howto perform merges or the like, in order to ensure smooth traffic flowand reduce an overall impact on efficiency of any conditions that tendto increase traffic.

Alternatively, or additionally, and further referring to FIG. 15 ,apparatus may communicate directly with self-driving vehicles, and/orone or more automatic components of vehicles. For instance, a vehiclehaving cruise control may receive a command to slow down from apparatusowing to dangerous conditions in which case vehicle may slow down.Alternatively, where vehicles are autonomous, apparatus may transmit tovehicle a modified driving plan, in which the vehicle slows down to someextent to deal with hazardous conditions, and or is alerted toparticular hazards which may be present on the ground.

In some embodiments, and further referring to FIG. 15 , apparatus mayreceive signals from drivers indicating that driers have received amessage transmitted thereto. For instance, and without limitation,drivers may perform a gesture such as a thumbs-up or other gestureindicative that they have received a message. Alternatively oradditionally, apparatus may determine that a driver is, or is not,complying with an instruction transmitted by apparatus using a behaviorclassifier and/or one or more sensors. For instance, where apparatus hasinstructed driver to slow down, apparatus may measure a speed ofdriver's vehicle, using doppler effect determinations using LIDAR and/orradar, or based on ToF distance calculations.

In some embodiments, apparatus 100 may be mounted on a drone. In anembodiment, distance detection of retroreflection may enable adrone-mounted apparatus 100 to fire a directed light deterrent atsufficient distance from a subject to arrive at a sufficiently shallowangle to enter a subject's eyes on-axis; alternatively, where drone isat an off-axis angle, for instance where it is more directly overhead,violet light may be used as described above to fluoresce subject's lens.Drone-mounted apparatus may detect which of multiple persons in a crowdis viewing drone using retroreflection, and/or when any given person islooking away and/or able to be affected by on and/or off-axis light. Inan embodiment, an apparatus and/or directed light deterrent mounted on adrone may determine a minimal angle from a vertical axis, calculated forinstance as a direction of a gravity vector using one or moreaccelerometers, at which a directed light deterrent may fire from thedrone; this angle may in turn determine a threshold distance from aperson on the ground past which the drone may be unable to file into theeyes of that person. This angle may include a drone angle, determined bya flight attitude of the drone, and a gimbal angle indicating an angleat which a directed light deterrent is able to fire; these angles may beadded to each other to attain an overall minimal angle. A third angle,which may depend on a head and/or eye position of a subject, maydetermine a minimal distance from the person from which a directed lightdeterrent may fire to strike a fovea or macula of the person; at a moreproximate distance, violet light may be employed to strike eyes forwhich the fovea is not available, if the drone remains outside theminimal distance determined by the drone and gimbal angles. Drone mayalternatively or additionally fire upon retroreflection detection of eyeaxes permitting access to fovea while outside minimal distance, and/ormay use sounds or other stimuli to direct a subject to look at drone.Drone may hover or circle outside minimal distance. A drone may maximizeits power budget using detection and/or “hide and seek” methodology asdescribed in this disclosure, such as without limitation by determiningwhen somebody's eye is open and looking at the system. LIDARretroreflection may assist in detection.

Alternatively or additionally an apparatus mounted in any mannerdescribed in this disclosure may be used to disable another drone, suchas one flying contraband into prisons or the like. Apparatus may be usedto aim at and/or detect drones, and may interdict with directed lightdeterrent to disable, saturate, and/or damage light sensors, and/or toaim projectiles such as bullets, nets, or the like. For instance andwithout limitation, a laser or other output may be used to confuseand/or blind sensors on drones, ships, aircraft, flying missiles, or thelike. Lasers can be used to burn out a vehicle's cameras withoutpursuing an aggressive action. In an embodiment, this may essentiallyblind drones and causes them to crash. Alternatively, a laser may“dazzle” optical systems of drones by saturation to wash out contrast,similarly to dazzle and/or veiling glare effects in human eyes asdescribed above. In an embodiment, a laser may be able to do both,combining dazzler capabilities with actual destruction.

Still referring to FIG. 15 , apparatus may use any suitable targetingand/or image analysis method to identify a drone and/or to identifyand/or strike optics thereof. For instance, and without limitation,methods for detecting a specular reflection and/or “glint” off of acornea and/or eyewear may similarly be used to detect specularreflection off a lens, transparent aperture cover, or the like.Alternatively or additionally, image classification and/or computervision may be used to identify a drone and/or identify a location ofoptics thereof. Classifiers may be trained with images of known dronescorrelated to identifications thereof and/or of optics thereon; theformer may be used to train a drone classifier to identify differentvarieties of drone, while the latter may train a drone optics classifierto locate optics on drones based on configuration and/or shape thereof.Classifiers may identify one or more locations that are more probablyoptics locations; apparatus may target all such locations simultaneouslyor substantially simultaneously.

Still referring to FIG. 15 , apparatus may use one or more behaviorclassifiers to determine whether a given drone is a threat and/or risk;for instance, behavior classifier may determine whether a drone isattempting to damage property, is crossing a restricted space where itcould collide with aircraft or sensitive equipment, or the like. Where adrone presents an immediate threat, apparatus may immediately interdict.Alternatively, apparatus may transmit warnings to drone using directedlight deterrent, audio signals, and/or any wireless communicationprotocol; such warnings may be directed to an operator of drone, and/orto an AI thereof where drone is a UAV.

Apparatus may be used to fire anything that may be targeted, includingBOLA wraps, nets, or the like; detection of body position may be used tofine-tune where and when to fire for maximum effect, such as firing atknees when mutually proximate to break a stride, firing at arms when ata subject's sides, or the like.

Further referring to FIG. 15 , apparatus 100 may be mounted in thevicinity of a person to monitor and/or protect the person. For instance,and without limitation, apparatus 100 may be mounted on a vehicle suchas a police car, in a corridor and/or room a person is in. Apparatus 100may protect person from a subject and/or subjects such as populations ofpersons who are incarcerated or otherwise under guard, for instance in acorrectional facility and/or psychiatric institution. In an embodiment,apparatus 100 and/or a network of apparatuses may operate as a “guardianangel” and protect a person within a subject area.

In an embodiment, and still referring to FIG. 15 , apparatus may use oneor more external devices (not shown) in the same manner as any imagingdevice and/or other sensor as described above. For instance, an externalcamera, or system of cameras, such as a security system or an elementthereof, may be capable of communication, for instance using a wirelessprotocol. Apparatus 100 may communicate with such a camera and/ornetwork to obtain visual data therefrom. For instance, apparatus 100and/or an additional computing device connected thereto may analyzevideo information from a network of cameras or other imaging deviceswithin a building or facility in which apparatus is installed; apparatus100 may perform this analysis continuously, for instance to identifypersons and/or other subjects within a larger building and/or facilitybeyond subject area. This may enable apparatus 100 to classify personsaccording to distinguishing features before and/or after arrival atsubject area, to classify behaviors thereof to determine threat levelsas described above, and/or to determine subsequent behavior after anencounter. Where a broader network of cameras and/or other apparatuses100 communicates with apparatus 100 and/or stores image data and/orclassifiers or parameters thereof, apparatus 100 may draw upon suchinformation, for instance and without limitation as described above.This may enable an extensive augmentation of sensory breadth and data tobe used by apparatus 100. For instance, it is estimated that of126,000,000 homes in the United States, 25% currently having a securitysystem. Such security devices systems experience a 4.5 million system ayear chum, with contracts burning off, people leaving, and/or attemptsat do-it-yourself projects by homeowners. It is further estimated thatanother 30,000,000 are being used in the commercial world. Communicationwith such devices may enable very extensive data to be available astraining data and/or resources for tracking and/or locating behaviors ofpersons. Use of existing security infrastructure may also furnish a wayto overcome installation costs where a more extensive network aroundapparatus 10 would be beneficial. Apparatus may transmit photos, video,current location of subject, or the like to law enforcement/military, orthe like; this may be provide as a feed, which may be encrypted.

In an embodiment, and continuing to refer to FIG. 15 , apparatus 100may, upon installation, start-up, reset, and/or initiation of a process,use one or more wireless and/or wired communication protocols to pollnearby devices. Communication protocols may include any protocols thatmay occur to persons skilled in the art, including without limitationthe BLUETOOTH protocol promulgated by the Bluetooth Special InterestGroup of Kirkland, WA, the ZIGBEE protocol developed by the ZigbeeAlliance of Davis, CA, or the like. Communication protocols may include,without limitation, any protocol used for exchange of data betweendevices, including ONVIF protocol as developed by Onvif, Inc. of SanRamon, CA for video streaming intercommunication. Apparatus 100 may pairwith such devices and use communications protocols to receive data fromand/or send data to such devices. Apparatus 100 may store in data ofapparatus 100 a registry of such devices; registry may alternatively oradditionally be received from a remote device, other apparatus 100 orthe like, permitting apparatus 100 to attempt to communicated and/orpair with devices already on the registry, remove them or update theirstatus on the registry based on attempted communication, and/or add newdevices to the registry, which may be transmitted in turn to remotedevices and/or apparatuses 100.

Still referring to FIG. 15 , apparatuses, devices, and/or methodsdescribed herein may be used for one or more additional applications,such as without limitation advertisement techniques; for instance,directed light deterrent may be used to “paint” an image of a productlogo, an advertisement slogan, a product name, trade name, trademark,service mark, or the like onto a person, object, structure, or the like.Image and/or behavior classifiers may be used, without limitation, tocapture one or more persons, objects, or the like of interest. Forinstance, where a “kiss cam” is being sponsored by a given commercialentity, advertisements of and/or directed by such entity may bedisplayed on or near a couple that is kissing on the “kiss cam”; soundeffects may be transmitted at the same time.

Further referring to FIG. 15 , system and/or apparatus may use sensorsto interact with people in singular or crowd settings. This may be usedfor art, entertainment, navigation, help, situational awareness, or thelike. For example, a version of the system and/or apparatus may beinstalled in a dance club; it may watch the dance floor, customize alaser and/or sound to react with a crowd as a whole or sections—forinstance, if a section is more active, it may get more laser showattention to either make them more active or entice other parts of thedance floor to increase their activity to get more attention for thelaser show and/or acoustics. Another example may include deployment at asports stadium such as a football stadium where the crowd starts a humanwave; system may project an ocean scene with a virtual surfer riding thehuman wave. Another example may include use of sensors to detect areaction of a crowd for voting purposes and display a sound meter abovea section of the crowd or other indicators. In a further non-limitingexample, system may write names of people on them at parties, so peopleknow who is who. As a further non-limiting example, apparatus be drawinginformation on a field at a stadium to demonstrate a football or otherplay, rules, examples, or the like in real time to observers in a manneranalogous to a virtual 1^(st) down line in television broadcasts. Athreat level and/or behavioral descriptor may include and/or becalculated based on a number of people in and/or near subject area, an eexistence of a crowd in or near subject area; for instance, a threatlevel may be determined to be elevated if more than a threshold numberof people are present, may be weighted by a weight depending on a numberof people present, or the like. As a further non-limiting example, aboundary condition applicable to a subject may be different when thesubject is alone than when some other number of people are in thesubject area.

In some embodiments, apparatus may be deployed as a light output for alight show. Light show may include, without limitation, an immersivelight show, in which images and patterns are cast around persons, and/oron bodies of persons, as opposed to solely over their heads. Immersivelight show may allow lasers to play across any part of users body,excluding the users' eye box as described above. In other words,apparatus may perform the same targeting procedure described above,including identification of eyes, light targeting area for eyes, or thelike, but may perform masking in reverse to avoid the targeted area.Apparatus may alternatively or additionally reduce intensity and/orexpand beamwidth when operating within a height range consistent withusers head heights, in order to prevent any unpleasant or potentiallyhazardous exposure through there to light such as laser light apparatusmay automatically detect and/or coordinate with music to perform lightshows.

In some embodiments, and still referring to FIG. 15 , light shows may beinteractive with behaviors of users. For instance, lasers may beprogrammed by apparatus to perform patterns complementary to dancingthat a person is performing. A person who appears to be dancingespecially effectively and/or actively may be highlighted using lasersor other light outputs, for example. Detection of effective, ornoteworthy, dancers may be performed by classifying behavior of peoplesurrounding the dancer. For instance, apparatus may include a behaviorclassifier that is trained using training data that contains behaviorsof persons who are observing another person dancing, where the observingperson has indicated enthusiasm, for the performance of the dancer.Alternatively or additionally, actions and are motions of a dancer, andor the people around them, may be entered as training examples with acorrelated identification of a level of ability and or achievement inthe dancing that expert or a group of users as indicated the dancerpossessed.

Still referring to FIG. 15 , behavior classifier may also identifyparticular behaviors of persons in the crowd, for example at a lightshow. For instance, where two persons appear to be getting physicallyintimate, they may be identified in a celebratory or admonishing manner,by apparatus. Apparatus may also perform any or all detection of userbehaviors such as problematic user behaviors that may be taking placeduring a light show, or other entertainment event. For instance, if aperson is becoming violent, or otherwise disruptive of the event,apparatus may identify what the person is doing. Apparatus may transmitnotification of the problematic behavior and/or identification of theoffender, to security to permit discreet interception and or removal ofthe offending party.

Further referring to FIG. 15 , one or more instances of apparatus 100may be deployed in an amusement park, theme park, or similar venue. Inan embodiment, apparatus or a network thereof may be deployed to performcrowd control, guidance, and/or amusement to persons therein, especiallywhile they are trying to make their way from one ride or attraction toanother. For instance, apparatus may detect where a person is heading,for instance by recognizing the person using facial recognition, andmatching them up to a subsequent reservation of a ride that the personhas booked. Apparatus may provide directions to persons who are lookingfor a next attraction, for instance by acting as a sort of interactivemap. Apparatus may draw and or right on the ground directionalinstructions for users who are attempting to make their way to one placeor another, including drawing instructions listing options fordirections users could go in, as well as interactive instructions thatmay detect a direction in which a user is heading, and provide the userwith further instructions and our feedback about where they are going,and what they are going to find their period alternatively oradditionally, apparatus may receive from a user in verbal or gesturalform, and or from a device belonging to users such as a smartphone orthe like, a user instruction indicating what sort of attraction the useris looking for, what attraction of the user is looking for, or the like.For instance, a user might enter a name of a particular rollercoasterthe user would like to ride on, and into their telephone, and or an appwhich may transmit the apparatus this identification. Apparatus maysubsequently identify user, and provide instructions written on theground in front of the user, which may be continually updated, andprovide indicators indicating which way the user should go and providingturn by turn or other instructions.

Still referring to FIG. 15 , apparatus, or a network of apparatus,deployed in an amusement park may generate outputs to entertain userswho are waiting in lines, or in other situations. For instance, users inline may be provided with interactive games, in which apparatus detectsmotion of a user, which motion may be interacting with images drawn byapparatus on a ground or surface or on other persons, and apparatus mayreact to user gestures interacting with such depictions, triggering suchdepictions to move and or react to user activity. For instance,apparatus Bay project a board and pieces of a board game on ground nearusers, permitting them to play a game of chess, checkers, or the like.Alternatively, apparatus may project an image of a ball, which a usermay pantomime kicking, which the apparatus may react to by causing theball to quote roll End Quote away from the user. Apparatus mayalternatively or additionally play messages and our music for individualusers in crowds, and lines, or otherwise making their way through and orstanding or sitting in amusement park, which messages may be userspecific using directed sound as described elsewhere in thisapplication.

In an embodiment, apparatus may alternatively or additionally provideusers in an amusement park with event information, such as informationindicating when a particular thought, production, parade, or the like isabout to take place. Such a show or other event may be providedaccording to a schedule, which may be available to and are transmittedto apparatus. Apparatus may write, display and or produce verbal and oraudio output indicative that this show is and or other event is about tostart.

Still referring to FIG. 15 , apparatuses, devices, and/or methodsdescribed herein may be used for one or more additional applications,such as without limitation entertainment systems. Entertainment systemsapplications may include, without limitation, use of stimuli describedabove as used for deterrents to induce reactions other than deterrenteffects, such as without limitation reactions of euphoria, amusement,suspense, or the like; reactions may induce an emotional, physiological,or chemical state in people receiving effects. Entertainment may includecolor and/or light effects. For instance, apparatus 100 may light uppeople who are walking around, dancing, or the like at concerts,nightclubs, or the like to enhance an experience at such a venue.Combined sensing and output functions of apparatus 100 may be used toproduce one of various game-like effects such as generation of simulatedobjects using light, such as a ball drawn on surfaces and/or media suchas mist by directed light output devices, which a user can bat, or kickas detected using sensor elements such as imaging devices as describedabove. As another example, during a laser show, laser may be turned offwhen scanning across eyes, for instance to avoid eyebox, head, and/orretroreflection.

Still referring to FIG. 15 , in a non-limiting embodiment, one or morelasers and/or other light output devices may be deployed in a swimmingpool, such as beneath the water and/or on the surface. Light sources maygenerate images, illumination, and the like beneath and/or on the water,on walls or floors of a pool, on bodies of one or more persons withinthe pool, or the like. Deployment, images, interactions with persons,and/or safety protocols may be performed in any way described in thisdisclosure.

With continued reference to FIG. 15 , an underwater deployment ofapparatus may be used to create light shows for swimming pools, lakes,or other bodies of water taking advantage of scintillation of laser inthe water for instance by leaving visible “laser beams” in the water.Apparatus may use a critical angle of laser incidence on surface ofwater to reflect back into the pool or out of the water to createabove/below light show, an ability to interact with people or objectsabove and below water, or the like. For instance based on persons'location, apparatus may draw “clothes” on them, draw on pool walls,and/or use multiple sources to create intertwined complex patterns inthe water, use peoples' motion to create interactive light show, or thelike. Underwater and above water systems may work independently ortogether to take advantage of laser interaction with water to createunique light shows, in addition to possibly combining with standardlaser projector to project completely above water. Underwater systemheat generating components may be physically coupled to a housing thatis exposed to pool water to provide heat dissipation, or water may beingested into system and pumped through for heat dissipation. System mayuse RF, audio, or optical wireless communications to control and/orcoordinate use of the system's single or multiple nodes under and abovewater. Safety features may be present to detect person approachingaperture and shut off using Time of Flight optical, ultrasonic, or otherrange measuring methods. System may use a single imaging sensor inconjunction with other systems to determine location of people orobjects below and above water. System may use lasers to create lightpatterns that can be used to further enhance a single or multipleimaging sensor ability to determine 3D position of people or objects inthe area.

In an embodiment, and still referring to FIG. 15 , system may useincreasing cadence, rhythm, color, or the like to indicate to a user toswim harder or lighter according to a predetermined intended pace, orthe like. System may project a “warning” light for arrival at an edgeduring backstroke, or to signal that a user doing laps is getting closerto an edge so the user can execute a flipturn.

With continued reference to FIG. 15 , apparatus when deployed in a poolmay detect a number of swimmers, and identity of each swimmer, and/or aprofile of each and/or any swimmer. A number of swimmers may be detectedby any suitable means described in this disclosure, including detectionof distinct persons using image classifiers, gradual reflection, and oridentification of shapes, anatomical landmarks, or the like.Alternatively or additionally, different swimmers may be detected bydetection of emotion. In an embodiment, identities of each swimmer maybededuced using image classification, voice classification, or the like.For instance, facial recognition may be performed to distinguish oneuser from another, and or other image recognition/image classificationmay be used to distinguish a body of one swimmer from another.Alternatively or additionally, image classifiers may be used to identifypeople according to larger or grosser categories, such as distinguishinga man over six feet tall from a woman five feet and four inches tall, oran adult from a child. These identifications may be compared to storeddata describing one or more persons known to use end or possess thepool. Identifications of types of users may include identifications ofpersons using the pool floor exercise, for recreation, for relaxation,for various forms of socialization including romantic encounters, or thelike. For instance, a person may be vigorously swimming from end one endof the pool to the other period in that case, apparatus may determinethat the person is engaged in athletic recreation within the pool. Inthis case, apparatus may perform detections and other processing asdescribed in further detail below, which are consistent with a personattempting to reach exercise and or athletic goals. As a further examplea person who is floating, and/or swimming lazily, particularly in agroup of people, may be detected and/or identified as a person who issocializing, or enjoying a party in the pool. Apparatus may furtherdetect other indicia such as music, conversation, or the like. Forinstance, a large amount of chatter in different voices, asdifferentiated using a voice recognition module, may indicate that alarge amount of conversation is taking place. This may be unlikely to beconsistent with athletic endeavors however it may be consistent withsocialization. Alternatively or additionally apparatus may identify atype of use to which one or another sectors of a pool, or an entirepool, it are being subjected. For instance, apparatus may identify thatlanes one through three of a pool are being used recreationally and/orfor relaxation, while lanes five through seven are being used for lapswimming, or diving. As a further example, apparatus may determine thatone person is engaging in a first kind of use, and a second person isengaging in a second kind of views, such as one person swimmingathletically, while another person floats on an inflatable raft drinkinga cocktail. Apparatus may interact with persons differently depending onwhat type of use those persons are attempting to engage in, asidentified by the apparatus. Alternatively, apparatus may receive voiceor manually entered commands, indicating that the pool is to be used fora party, is to be used for an athletic endeavor of some sort, or thelike.

Still referring to FIG. 15 , apparatus may perform processing consistentwith detected identities of persons, types of users, and/or types of useto which the pool is being subjected. For instance, apparatus may detectthat a person is attempting to swim laps. Apparatus may provide quotevirtual physical training and quote to such a person in the form oftiming of laps, encouraging voice outputs, add or recordation, analysis,and recommendations regarding strokes a user is swimming. For example, auser who is swimming a freestyle or “crawl” stroke maybe identified by abehavior classifier which notes and or determines that their arms arebeing raised above the water add in front of their heads and arepetitive motion, perhaps combined with kicking at the surface of thewater with a straight legged kick, and reading to the side.Classification may account for sufficient variation in performance of astroke to identify that some of those attempting to perform a freestylestroke whether or not they're doing so correctly. In an embodiment, afirst classification process may be used to determine what kind ofstroke a user is attempting to perform, and a second classificationsprocess may calculate an error function to determine how closely thatperson is mimicking a correct or ideal form for that stroke. Forinstance, a person whose breathing technique causes them to modify theirstroke to an unacceptable extent, as determined by deviation from aprofessional swimmers stroke pattern well breathing, may be identifiedas deviating in that respect from an ideal freestyle stroke. In thiscase, apparatus may provide feedback to the user. Feedback may beprovided during exercise, between sets, laps, or the like, or after asession. For instance, apparatus may generate, display, or print areport indicating how a user performed when engaging in strokes, orother endeavors they were engaging in. Alternatively, a voice outputdevice of apparatus, which may include any voice output device asdescribed above, may generate encouragement, recommended changes tofarm, reminders to maintain and or modify a given approach to a givenstroke, or the like. Apparatus may generate recommendations based on aparticular user. For instance, apparatus may use a classifier trained bytraining examples collected from a particular user to identify how theuser has typically performed, and may calculate error functions todetermine a difference between that performance and an ideal performanceof a given stroke or other maneuver. This error function made then becalculated for current user strokes. In this case, apparatus may setgoals for a user to reduce an error function historically calculated.For instance, apparatus may identify a particular kind of deviation froman ideal stroke that a user performs, and may identify one of a numberof such deviations which is especially great compared to others,indicating that that is a particular facet of the stroke that the usershould be attempting to improve. Feedback and/or recommendations to userprovided during, before, or after exercise may include a larger quantityof feedback pertaining to that particular facet of a particular strokethat a user most needs to improve in. Alternatively or additionally,apparatus may compare a time such as a lap time that a user performs, intwo previous lap times, and or two goals. Goals may be set by the user.Alternatively, goals may be set by apparatus, based on a typicalimprovement curve from 1 day to another during a training regimen. Suchan improvement curve may be determined using classifiers, machinelearning, or the like, which may be trained using training data that hasrecorded previous users times, or other performance parameters duringprevious training sessions, or sequences thereof. A user may alsoindicate to apparatus, through voice command, manual entry, and/orselection of one or more options in a graphical user interface, whichparticular kind of thing the user wants to improve on an exercise. Forinstance, one user may enter command indicating that that user wishes towork on a speed of swimming, and or on reducing lap time, while anotheruser may wish to engage in improvement on one or more forms of one ormore strokes, or some combination, such as lap times as performed usinga particular stroke. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various alternative oradditional ways in which users and or apparatus may specify and aremeasure performance against goals.

Still referring to FIG. 15 , an additional application of embodiments,apparatuses, devices, and/or methods described herein may include usethereof diagnostically to determine sensitivity of a person to differentstimuli; for instance, and without limitation, apparatuses, devices,and/or methods described herein may be used to produce various strobingeffects at increasing intensities to assess a predilection for seizuresof a given individual, which may be used to determine fitness for likelyseizure-inducing environments, as a screening tool to diagnose epilepsyand/or tendencies therefor, or the like. Apparatuses, devices, and/ormethods described herein may be used to administer gradually escalatingsensory stimuli in various forms to determine a degree of sensitivity ofa person to particular sensory stimuli; for instance, a person on theautistic spectrum and/or with a diagnosis or potential diagnosis ofautism spectrum disorder (ASD) may be subjected to a carefullyadministered test to assess different reactions to stimuli and aid ingeneration of a “sensory diet.” Reactions to stimuli may be determinedusing behavior classifiers as described above, which may be trainedusing training data pertaining to persons sharing characteristics of asubject persons; such training data may be classified using a trainingdata classifier as described above.

Further referring to FIG. 15 , apparatus may be deployed in a handheldform. In handheld form, apparatus may automatically generate deterrentsin response to different behavioral profiles as described above, or mayproduce deterrence when triggered manually, for example by setting thethreat level, using a dial or the like on the device, and or pulling atrigger.

Further referring to FIG. 15 , apparatus 100 may be deployed on a boat.Boat apparatus may be oriented such that sensors are oriented at anelevated angle to intersect any incoming signals from a horizon and/orfocal point, wherein deterrents may be angled at a reduced angle towardsa medium underneath the boat. Additionally or alternatively, apparatus100 may be deployed on an aircraft. Aircraft apparatus may includecalculating a gravity vector as a function of an accelerometer. As usedin this disclosure a “gravity vector” is a data structure thatrepresents one or more a quantitative values and/or measures of gravitywith relation to the aircraft. A vector may be represented as an n-tupleof values, where n is one or more values, as described in further detailbelow; a vector may alternatively or additionally be represented as anelement of a vector space, defined as a set of mathematical objects thatcan be added together under an operation of addition followingproperties of associativity, commutativity, existence of an identityelement, and existence of an inverse element for each vector, and can bemultiplied by scalar values under an operation of scalar multiplicationcompatible with field multiplication, and that has an identity elementis distributive with respect to vector addition, and is distributivewith respect to field addition. Each value of n-tuple of values mayrepresent a measurement or other quantitative value associated with agiven category of data, or attribute, examples of which are provided infurther detail below; a vector may be represented, without limitation,in n-dimensional space using an axis per category of value representedin n-tuple of values, such that a vector has a geometric directioncharacterizing the relative quantities of attributes in the n-tuple ascompared to each other. Two vectors may be considered equivalent wheretheir directions, and/or the relative quantities of values within eachvector as compared to each other, are the same; thus, as a non-limitingexample, a vector represented as [5, 10, 15] may be treated asequivalent, for purposes of this disclosure, as a vector represented as[1, 2, 3]. Vectors may be more similar where their directions are moresimilar, and more different where their directions are more divergent;however, vector similarity may alternatively or additionally bedetermined using averages of similarities between like attributes, orany other measure of similarity suitable for any n-tuple of values, oraggregation of numerical similarity measures for the purposes of lossfunctions as described in further detail below. Any vectors as describedherein may be scaled, such that each vector represents each attributealong an equivalent scale of values. Each vector may be “normalized,” ordivided by a “length” attribute, such as a length attribute l as derivedusing a Pythagorean norm: l=√{square root over (Σ_(i=0) ^(n)a_(i) ²)},where a, is attribute number i of the vector. Scaling and/ornormalization may function to make vector comparison independent ofabsolute quantities of attributes, while preserving any dependency onsimilarity of attributes.

In some embodiments, and still referring to FIG. 15 , apparatus 100 maybe used for one or more additional processes such as temperature checksduring pandemics, or during other health emergencies, apparatus 100 maycheck subject 308 body temperatures using infrared camera 112, sensechemicals or indicators emitted by the subject 308, or the like, where aperson having a detected body temperature above a preconfiguredthreshold may be warned not to proceed through subject, may be deterredusing any means described above, or may be otherwise prevented fromcontacting people whom such a person might infect. Alternatively oradditionally, a detected person having a high body temperature mayresult in a message being transmitted to security, or other devices, toprevent such a person from coming into contact with other people thatthey may in fact. Mitigation responses may include activation ofelectronic locks to prevent entering into further rooms near subjectarea, as well as transmission of messages to medical professionalsnearby. In addition, past data from the remove device 140 can be used tocontact trace a person who may be detected to have been infected byreviewing all people identified in imagery that were in close proximityto the subject 308.

Referring now to FIG. 17 , an exemplary embodiment of a method 1700 ofautomated threat detection and deterrence is illustrated. At step 1705,identifying, by a processor 136 communicatively connected to an imagingdevice 104 and a deterrent component 152 including a directed lightdeterrent 156, a subject 308 as a function of a detection of the subject308 by the imaging device 104; this may be implemented, withoutlimitation, as described above in reference to FIGS. 1-12 . At step1710, determining, by the processor 136, a behavior descriptorassociated with the subject 308; this may be implemented, withoutlimitation, as described above in reference to FIGS. 1-12 . At step1715, selecting, by the processor 136, a mode of a first deterrent modeand a second deterrent mode as a function of the behavior descriptor;this may be implemented, without limitation, as described above inreference to FIGS. 1-12 . At step 1720, commanding, by the processor136, the directed light deterrent 156 to perform an action of a firstdeterrent action and a second deterrent action as a function of themode, wherein the first deterrent action is distinct from the seconddeterrent action; this may be implemented, without limitation, asdescribed above in reference to FIGS. 1-12 .

Disclosed herein are various embodiments of a deterrent apparatus withmultiple deterrent types and an incorporated watchdog system thatmonitors parameters of deterrents and/or other apparatus elements toensure adherence to safety standards. Cumulative outputs may be trackedacross mesh networks of multiple apparatuses. Shutdown or limiting ofdeterrent outputs may be performed autonomously from other elements,adding redundancy to safety protection measures.

Referring now to FIG. 18 , an exemplary embodiment of a multimodaldeterrent apparatus 1800 with an internal watchdog system isillustrated. Apparatus 1800 includes a deterrent suite 1804. A“deterrent suite,” as used in this disclosure, is a set of one or moredeterrent components. A “deterrent component,” as used in thisdisclosure, is an electronic component that includes and/or drives adeterrent, defined as an element or device that generates a deterrentoutput. A “deterrent output” is defined of the purposes of thisdisclosure as a physical or psychological interaction with an individualthat discourages and/or stops the individual from performing a behaviorcontrary to one or more security objectives; the one or more securityobjectives may be determined by apparatus 1800 and/or entered thereon bya user, for instance and without limitation as described in U.S.Provisional Application No. 63/067,142. A deterrent component mayinclude additional elements as described in further detail below.

With continued reference to FIG. 18 , deterrents included in deterrentsuite 1804 may include, without limitation a directed light deterrent. A“directed light deterrent,” as used in this disclosure, is a deterrentthat uses a high-intensity light source, such as, but not limited to, alaser, super LED, laser illuminated LED, super-luminescent LED, VCSEL,plasma discharge lamp, and/or high-intensity LED that is actively aimedat and/or focused on an individual to be deterred, to generate adeterrent effect. A directed light deterrent may include a beam steeringcomponent, which may include, without limitation, two or more reflectiveelements used as scanning mirrors, spatial light modulators,metamaterials/metasurfaces, liquid crystal directors, Risley prisms,micro-optical arrays, fast steering mirrors, tip/tilt optics,holographic phase modulators, and/or off-centered lens elements. In oneembodiment, reflective elements, which may include any reflectiveelements for use in scanning mirrors as described above in reference tolight radar component, may be arranged in close proximity to one anotheron axes that are substantially orthogonal causing one mirror to act as avertical scanning mirror and another mirror to act as a horizontalscanning mirror. Such an arrangement may enable rapid scanning of laserand or other light beams across objects in subject area. Directed lightdeterrent may include any additional optics suitable for use in opticalinstruments such as lasers or other high intensity light sources,including additional amplifiers, beam expanders, or the like. In anembodiment, a beam may be collimated, or may not be collimated at one ormore different stages in its processing by optical instruments withindirected light deterrent. Light from directed light deterrent may becoherent or may not be coherent, depending on desired applications. Insome embodiments, optical elements through which a beam may pass indirected light deterrent may have an effect of dissipating, polarizing,wavelength shifting, filtering, modifying, homogenizing, interrupting,or spreading power of the beam. As a result, a beam incident on objectsin subject area, including without limitation a face or eyes of anindividual, may have substantially lower intensity than at initialproduction of beam.

Still referring to FIG. 18 , deterrent suite 1804 may include amicrowave or MMW source. Microwave or MMW source. Microwave source mayemit light and/or radiation at a wavelength that causes non-lethal painand/or burning sensations; for instance, and without limitation,microwave and MMW source may generate radiation having a frequency ofapproximately 95 GHz.

Further referring to FIG. 18 , deterrent suite 1804 may include an audiodeterrent, for instance and without limitation as defined above; audiodeterrent may include a directed sound source and/or a directed audiodeterrent.

With continued reference to FIG. 18 , deterrent suite 1804 may includean electric shock or Human Electro-Muscular Incapacitation (HEMI)device. Electric shock or HEMI device may include a “stun gun” and/ortaser, which may, for instance be able to fire two or more cartridges upto 25 feet, as in a taser area denial system (TADS). Alternatively oradditionally, shock device may be used to electrify one or more objectsor surfaces to generate a shock upon contact or near approach by a user.Alternatively or additionally, shock and HEMI device may use generatedplasmas, electric fields, ionization, and/or other methods to convey aneurostimulation to an individual from a distance.

With further reference to FIG. 18 , deterrent suite 1804 may include oneor more sources of noxious smells and/or other chemical deterrents, suchas pepper spray, “skunk” or malodorant weapons, tear gas, pacifyingagent, or the like, which may be squirted out of nozzles in liquid,vapor, and/or gaseous form, or fired as projectiles that break openand/or otherwise disperse such irritants. Chemical deterrents mayfurther take a form of a sticky and/or slippery substance released ontoa walking surface to make proceeding and/or aggressive motions moredifficult.

Still referring to FIG. 18 , deterrent component may include one or moresources of entanglement devices such as without limitation nets, bolas,and/or other entanglement or entrapment devices that are launchedballistically at an individual in order to limit or stop an individual'sability to move normally. A deterrent component may use a processor 1844and/or imaging devices to calculate and predict, based on the distanceand movement of an individual, in addition to the ballistic propertiesof the entanglement device, a corrected point of aim to launch theentanglement device.

With further reference to FIG. 18 , deterrent suite 1804 may include oneor more sources of obscurant delivery system, such as, but not limitedto, devices that operate to obscure vision or other senses of anindividual. For instance, and without limitation, these may include foggenerators such as biodegradable fog generators, smoke generators,and/or water mist generators. An effect of obscurant deterrents may befurther enhanced by illuminating obscurants with light sources, whichmay include any light sources of deterrent suite 1804 as described inthis disclosure.

With continued reference to FIG. 18 , deterrent suite 1804 may includeone or more sources of blunt force or kinetics delivery devices, suchas, but not limited to, bean bag round launchers, stingballs, non-lethalgrenade or payload launchers, water cannons, air cannons, and/or othermethods to deliver non-lethal kinetic effects to an individual.

Still referring to FIG. 18 , deterrent suite 1804 may include one ormore sources of marking delivery devices, including without limitationpaintball launchers, dye sprayers, paint sprayers, malodorant launchers,and/or other methods that will optically, odorant, or other senses tagan individual for later identification. Marking delivery devices mayinclude dyes that are visible or non-visible to the human eye thatrequire special lighting or other methods to detect at a future time.Dyes, paints, and/or markers may be formulated to make removal extremelydifficult.

In an embodiment, and further referring to FIG. 1 , deterrent suite 1804may be configured to combine one or more types of deterrentsimultaneously and/or sequentially, for instance to create a flankingeffect, or as dictated by power, resource limitation, and/or safetydeterminations. For instance, optical deterrent actions may be combinedwith startling noises and/or commands to vacate subject area and/orcease one or more activities. Alternatively, if an individual hasrecently had vision temporarily impaired, directional audio outputs maybe used to urge an individual toward an exit and/or to increasedisorientation of an individual. Which sequence and/or combination ofdeterrents is used may be determined using an input from watchdogelements as described in further detail below.

Further referring to FIG. 18 , deterrent suite 1804 may include aplurality of deterrent components, each of which may include a differentdeterrent; for instance, in a non-limiting embodiment, deterrent suite1804 includes a first deterrent 1816 component 1808 and a seconddeterrent 1820 component 1812. First deterrent 1816 component 1808 andsecond deterrent 1820 component 1812 include, respectively, a firstdeterrent 1816, and a second deterrent 1820, each of which may includeany deterrent as described above. Second deterrent 1820 may be distinctfrom first deterrent 1816. For instance, and without limitation, firstdeterrent 1816 may include a directed light deterrent while seconddeterrent 1820 may include an audio deterrent.

Further referring to FIG. 18 , first deterrent 1816 component 1808 andsecond deterrent 1820 component 1812 include, respectively, a firstoutput reduction element 1824 and a second output reduction element1828. An “output reduction element,” as used in this disclosure, is acomponent and/or element of a deterrent component that, when activated,attenuates, interrupts, and/or terminates output by a deterrent of thedeterrent component. An output reduction element may provide enhancedsafety characteristics for deterrent suite 1804 and/or apparatus 1800 asa backup and/or failsafe deterrent interruption based on operatingparameters of the apparatus 1800. For instance, and without limitation,where a deterrent of a deterrent component is a directed lightdeterrent, an output reduction element may include a shutter or similardevice that interrupts the outputted light source. For instance, andwithout limitation, a shutter may include an LST400 from NM LaserProducts; in an embodiment, a shutter may be able to completely preventall or substantially all light output within 20 milliseconds of command.A shutter may prevent passage of a beam of light such as a laser beam inthe absence of a command signal directing the shutter to remain open,such that a fault and/or interruption in communication with shutter maycause an automatic closure thereof. As a further example, where firstdeterrent 1816 component 1808 includes a directed light deterrent, firstoutput reduction element 1824 may include an optical modulator, such aswithout limitation an Acousto-optic modulator or electro-optic modulatorsuch as those used in q-switching or the like. For instance, and withoutlimitation, an optical modulator may include a polychromaticacousto-optic modulator. Additional non-limiting examples of outputreduction elements that may be used with a directed light deterrentinclude filters, attenuators, physical interruptions, or the like

As a further non-limiting example, and continuing to refer to FIG. 18 ,at least one of first output reduction element 1824 and second outputreduction element 1828 may include a power regulation control element. A“power regulation control element,” as used in this disclosure, is anelement activation of which restricts electrical power to a componentsuch as a deterrent as described above. A power regulation controlelement may include one or more power switches, current and/or voltagelimiters, or the like. Power regulation control elements may beimplanted using physically actuated relays, transistors such as bipolarjunction transistors (BJTs), insulated gate bipolar transistors (IGBTs),field effect transistors such as metal oxide field effect transistors(MOSFETs), thyristors such as integrated gate-commutated thyristors(IGCTs) or triodes for alternating current (TRIACs), variable resistorssuch as photoresistors, thermistors, potentiometers, or the like, and/orany other device that may be used to switch on or off or otherwiseregulate electric current.

Further referring to FIG. 18 , where one of first deterrent 1816 andsecond deterrent 1820 includes an audio deterrent, at least one of firstoutput reduction element 1824 and second output reduction element 1828may include a sound-attenuating element. A “sound-attenuating element,”as used in this disclosure, is an element that physically interfereswith emission of sound by an audio output device. A sound-attenuatingelement may include, without limitation, a door, blanket, or pad thatcan be closed over a speaker or other audio output device, and/or anobject that can be brought into contact with a membrane and/orpiezoelectric element of a speaker, and/or a mechanical mechanism thatcan move a piezoelectric element out of contact with a membrane or otheramplifying medium.

Still referring to FIG. 18 , apparatus 1800 includes an internalwatchdog system 1832. Internal watchdog system 1832 and/or one or moreelements thereof as described in further detail below may beincorporated in apparatus 1800 by inclusion within a housing containingother elements of apparatus 1800. Internal watchdog system 1832 and/orone or more elements thereof may draw on a power source of apparatus1800. Internal watchdog system 1832 may include one or more elementssharing and/or electrically connected to one or more electrical circuitelements of apparatus 1800. Internal watchdog system 1832 may includeone or more elements communicatively connected to elements of apparatus1800. As used herein, “communicative connecting” is a process wherebyone device, component, or circuit is able to receive data from and/ortransmit data to another device, component, or circuit. In anembodiment, communicative connecting includes electrically coupling atleast an output of one device, component, or circuit to at least aninput of another device, component, or circuit. Communicative connectionmay be wired, wireless, effected using magnetic and/or opticalcouplings, or the like; communicative connection may be performedaccording to any process and/or protocol for communication betweendevices and/or units as described in this disclosure.

With continued reference to FIG. 18 , internal watchdog system 1832includes a detector component 1800 configured to detect a firstparameter of the first deterrent 1816 component 1808 and a secondparameter of the second deterrent 1820 component 1812. Detectorcomponent 1800 may include one or more sensors or other elementsconfigured to measure parameters of performance of one or more elements,components, outputs, and/or inputs of apparatus 1800. Detector component1800 may include a single interconnected component and/or two or moredisconnected components; alternatively or additionally, internalwatchdog system 1832 may include multiple instances of detectorcomponent 1800. As a non-limiting example, where first deterrent 1816includes a directed light deterrent, first parameter may includeirradiance generated by the directed light deterrent. For instance, andwithout limitation, where measuring a directed light deterrent, anoptical power meter may include a measuring head such as model numberSEL033 and an optical amplifier board such as model A430, both from thecompany International Light. An optical power meter may be used toconstantly monitor the power of a laser beam or other beam being emittedfrom a directed light deterrent. Optical power meter may detect power ofa mean using a pick-off and/or beam splitter-based optical power meteror may include a pass-through optical power meter.

As a further example, and continuing to refer to FIG. 18 , where seconddeterrent 1820 includes an audio deterrent, second parameter may includea measure of acoustic intensity, such as a level of acoustic intensityrepresented using decibels safety with regard to human hearing.

Still referring to FIG. 18 , where a deterrent of first deterrent 1816component 1808 and second deterrent 1820 component 1812, and/ordeterrent suite 1804, includes a scanning mechanism, such asgalvanometric mirrors, FSM mirrors, or the like, a parameter related tosafety may include a scanning velocity. A detection component may beconfigured to act as a velocity monitor, which may monitor X-Y scanningaction. As a non-limiting example, X and Y position signals from X-Ygalvanometric and/or scanner servo drivers may be input toanalog-to-digital conversion circuitry connected to logic circuits,and/or to operational amplifiers which are configured to take a firstderivative of each position signal, thus providing signals which areequivalent to the X and Y beam velocity. X and Y beam velocity signalsmay then be aggregated to produce a number representing overall scanningvelocity; aggregation may include addition of absolute values of X and Yvelocity, for instance by finding twos complements of negative values,and/or by storing X and Y velocities in unsigned number fields asdirectionless “speed” variables. Alternatively or additionally, X and Ybeam velocity signals may be squared by multipliers. Aggregate signalmay be formed by adding together absolute value and/or squared velocityvalues. This signal may then be compared with a constant voltage whichrepresents a preferred minimum acceptable beam velocity, as described infurther detail below. An aggregate value representing X and Y positionmay alternatively or additionally be monitored.

With continued reference to FIG. 18 , at least one of the firstparameter and the second parameter may include an electrical parameter.An “electrical parameter,” as used in this disclosure, is any parameterthat may be directly or indirectly measured with regard to an electricalcircuit. For instance, and without limitation, an electrical parametermay include a voltage level. As a further non-limiting example, anelectrical parameter may include a current. Other electrical parametersmay include, without limitation, capacitance, inductance, resistance, orthe like. Electrical parameters may include voltage, current, and/orpower to particular deterrents. For instance, voltage, current, and/orelectrical power to a deterrent that converts electrical power to aproportionally related output, such as a directed light deterrent, anaudio deterrent, an electrical deterrent, or the like, may be used torepresent power being output by such deterrents; values so derived maybe used as substitutes for and/or in addition to devices that directlymeasure output such as meters of light intensity, sound intensity, orthe like.

Still referring to FIG. 18 , a detector component 1800 measuring anelectrical parameter may include a power supply detection component. Apower supply detection component may monitor the power supply voltagesthat feed one or more deterrents and/or an output from a power supplycomponent such as a connection to alternating current mains power, anenergy storage device such as a battery or fuel cell, a photovoltaicpower source, or the like; power drawn from a power source may becompared to power drawn by components, for instance and withoutlimitation to detect faults in apparatus 1800 and/or detector elementcircuitry.

With continue reference to FIG. 18 , a detector component 1800 maymeasure an electrical parameter of another detector component 1800. Forexample, and without limitation, where a first detector component 1800measures intensity of a deterrent output, a second detector component1800 may measure one or more electrical parameters of the first detectorcomponent 1800 to determine whether the first detector component 1800 isfunctioning effectively; if first detector component 1800 is notfunctioning correctly, an output reduction element corresponding to thedeterrent may be activated to prevent potential safety issues. As anon-limiting example, a beam power meter monitor may measure an outputof a beam power meter as described above, and make sure that it isfunctioning properly.

Still referring to FIG. 18 , at least one of first parameter and secondparameter may include a temperature. Temperature may be measured,without limitation, using an electrical component for which at least oneelectrical parameter changes as a result of a change in temperature,such as without limitation a thermistor or the like. In an embodiment, atemperature of a circuit element may be used to indicate likelyperformance of that element; for instance, semiconductor elements suchas transistors, diodes, LEDs, or the like may function differently whenoverheated then when operating at ordinary temperatures. As a furthernon-limiting example, a temperature of a deterrent may indicate poweroutput and/or may be compared to electrical power input to the deterrentcalculate power output by measuring waste heat generated by thedeterrent. As an additional non-limiting example, a temperature of aparticular component above a preconfigured threshold may indicate thatthe component is about to fail or is likely to behave in anunpredictable or inefficient manner.

With continued reference to FIG. 18 , at least one of first parameterand second parameter may include a cumulative energy value. A“cumulative energy value,” as used in this disclosure, is a valuerepresenting a total amount of energy delivered by a deterrent over agiven time period. For instance, and without limitation, maximumpermissible exposure for a directed light deterrent as described infurther detail below may specify a maximum instantaneous intensity,irradiance, and/or power delivery, and/or may specify a maximum totalenergy which may be delivered to the eyes of an individual, where thelatter may be compared to a cumulative energy value representing a totalquantity of energy delivered by a directed light deterrent. As anadditional non-limiting example, electrical deterrents may have bothinstantaneous limits on voltage and/or current as well as overallelectrical power delivery limits, the latter of which may correspond toa cumulative energy value representing a total electrical energydelivered. As a further non-limiting example, an acoustic deterrent maybe subject to a maximum instantaneous measure of intensity such as adecibel level, as well as a cumulative limit of decibels above athreshold level that are delivered over a given time period. Acumulative energy value may depend both on time and intensity ofexposure; that is, exposure beneath a threshold of power or intensitymay not be added to the cumulative energy value.

In an embodiment, and still referring to FIG. 18 , a cumulative energyvalue may include a per-engagement value. A per-engagement value, asused in this disclosure, is a cumulative energy value that accumulatesover the course of an engagement. An “engagement,” as used herein, is asingle interaction or sequence of interactions of a particularindividual with apparatus 1800 or a network or system containingapparatus 1800. For instance, an engagement may include a period of timeduring which an individual is attempting to access a forbidden orprotected zone, is trespassing on a premises being protected byapparatus 1800, and/or is otherwise engaging in behaviors that causeapparatus 1800 to use deterrents against the individual. An engagementmay include an uninterrupted period in which an individual is in asubject area protected by apparatus 1800 and/or a system includingapparatus 1800 and/or may include a series of such uninterrupted periodsthat are temporally proximate. For instance, apparatus 1800 and/or acomponent thereof may include a variable that is set when interactionwith individuals and/or a particular individual begin, indicatinginitiation of an engagement, and may be cleared and/or reset, indicatingthe end of an engagement, when interaction generally or with a specificindividual has ceased for a threshold period of time. For instance,termination of an engagement may be recorded when a given person hasbeen absent from and/or not interacting with apparatus 1800 for onehour, one day, or any other suitable period. In an embodiment, eachvariable may receive initiation and reset signals, and/or signalsidentifying a particular individual as currently interacting, fromapparatus 1800, permitting use of facial recognition and/or other datadetermined by apparatus 1800 to be used in determining whether a givenindividual is currently interacting with is currently adding to acumulative energy value while a corresponding deterrent is outputting.In such an exemplary embodiment, where apparatus 1800 is not identifyingparticular individuals, all interactions may be treated as correspondingto a single individual; that is, cumulative energy values may dependsolely on outputs generated during an engagement as delimited bycessation of all interaction for a threshold period of time.Individual-agnostic cumulative energy values and/or per-engagementenergy values may alternatively or additionally be used as a fail-safelimit to prevent accidentally exceeding cumulative energy values due tofaulty recognition of distinct individuals.

With further reference to FIG. 18 , cumulative values may be tracked andrecorded across a mesh network. Mesh networks may be used to coordinateresponses between two or more apparatuses 1800. For instance, twoapparatuses 1800 in the same subject area may coordinate transmission ofdirected light deterrent actions, or other actions based upon detectedindividual behavior, postures, or the like. For instance, and withoutlimitation, two or more apparatuses 1800 may have two or more deterrentlight wavelengths which may be deployed concurrently or sequentially inorder to add to confusion and/or resistance to eyewear protection asdescribed above. Alternatively or additionally, two or more apparatuses1800 deployed in two or more separate security zones and/or subjectareas may coordinate by communicating actions and/or determinationsconcerning entrance and/or intrusions in such security areas. This maybe used, for instance, to determine what ambient light exposure anindividual has experienced, which direction an individual has come from,and/or what activity an individual may be expected to perform. Forinstance, where one apparatus 1800 has detected aggressive behavior byan individual, this may be used as an immediate blacklist by otherapparatuses 1800, where an individual identified as the same individualentering a new subject area may be immediately responded to with moreaggressive responses such as saturation, strobing, electric shock orother responses, on the basis that this individual has been identifiedas a threat that must be neutralized. Such data may also be transmittedremotely, and sent as updates to security teams, law enforcement, orother users attempting to respond to an ongoing or developing securitythreat. Such user may use such information to determine a likely currentlocation of a perpetrator and or other individual as well as toformulate or plan a strategy for counteracting the actions of anindividual and neutralizing any threat. Two or more apparatuses 1800deployed in the same area may be used to create one or more additionalcoordinated actions, such as creation of light curtains, to indicatedivisions between authorized and unauthorized areas, guide crowdmovement, or the light. As a further example, a series of apparatus 1800is may provide directional indicators such as directional images or thelike which made direct entrance and our users in Direction Evacuation,or the like.

In an embodiment, and still referring to FIG. 18 , each internalwatchdog system 1832 may transmit cumulative values to other apparatuses1800 in mesh networks. Internal watchdog system 1832 may receive initialand/or updated cumulative energy values from other apparatuses 1800. Inan embodiment, internal watchdog system 1832 may add such updatedcumulative values to corresponding individual and/or individual-agnosticcumulative values maintained by internal watchdog system 1832;alternatively or additionally, updated values received from one or moreadditional apparatuses 1800 in a mesh network may overwrite currentcumulative values of internal watchdog system 1832, optionally onlywhere the former is greater than the latter. In an embodiment, trackingand incrementing of cumulative energy values across mesh networks mayhelp to ensure that apparatuses 1800 across a mesh network do notcombine to exceed cumulative safety limits.

With continued reference to FIG. 18 , at least one of first parameterand second parameter may include a location-dependent parameter. A“location-dependent parameter,” as used in this disclosure, is acumulative parameter that applies for some region of an area or volumethat may be scanned by a deterrent such as without limitation firstdeterrent 1816 or second deterrent 1820. For instance, a region acrosswhich a deterrent may scan may be divided into sectors, identifiable byinternal watchdog system 1832 by measurement of scanning x and y values,for instance as described above; a total plane and/or volume of scanningmay be divided into sectors, and cumulative values may be takenper-sector. Alternatively or additionally, apparatus 1800 may provide acurrent sector containing an individual to internal watchdog system1832, and cumulative energy values for that individual may be recordedper the indicated sector.

Still referring to FIG. 18 , at least one of first parameter and secondparameter may include a distance-dependent parameter. A“distance-dependent parameter,” as used in this disclosure, is aparameter that depends on a distance from an individual to apparatus1800. For instance, and without limitation, an individual 10 feet (3 m)away from a directed light deterrent may receive a higher amount ofoptical energy than individual 20 feet (6 m) away from the directedlight deterrent, per output irradiance, owing to beam dispersal,diffraction, and the like. Apparatus 1800 may provide internal watchdogsystem 1832 with a value indicating a current distance from apparatus1800 of an individual. Internal watchdog system 1832 may weightmeasurements taken for cumulative and/or instantaneous values accordingto value.

With continued reference to FIG. 18 , at least one of first parameterand second parameter may include an aggregate parameter. An “aggregateparameter,” as used in this disclosure, is a parameter calculated as afunction of two or more parameters as described above. Aggregation mayinclude addition, averaging, or any other form of combination of values.As a non-limiting example, a plurality of parameters regarding onedeterrent may be aggregated together to generate an aggregate parameterwhich may be used as an alternative or additional parameter to theindividual parameters. For instance, scanning velocity, outputintensity, and/or other parameters corresponding to a directed lightdeterrent may be combined to generate an aggregate parameter which maybe measured in its own right.

Continuing to refer to FIG. 18 , internal watchdog system 1832 includesa control component 1840. Control component 1840 may include analogcircuit such as a circuit that performs computations and/or comparisonsusing operational amplifiers and/or comparators. Control component 1840may include a logic circuit, such as a field-programmable gate array(FPGA), (ASIC), processor 1844, microcontroller, or the like. Controlcomponent 1840 may receive reference values, cumulative values,initiation signals, reset signals, firmware updates, and/or softwareupdates from remote devices 1848 and/or elements of apparatus 1800, forinstance and without limitation as described below. Control component1840 is configured to compare each of the first parameter and the secondparameter to a reference value and activate at least one of the firstoutput reduction element 1824 and the second output reduction element1828 as a function of the comparing. A “reference value,” as used inthis disclosure, is a value representing a safety limit to which aparameter may be compared to determine whether a safety limitcorresponding to one or more deterrents has been reached. A referencevalue may be stored in local hardware and/or software memory of internalwatchdog system 1832, for instance during manufacture, installation,and/or calibration of internal watchdog system 1832 and/or apparatus1800. Alternatively or additionally, control component 1840 may beconfigured to receive reference value from a remote device 1848. Controlcomponent 1840 may be configured to receive reference value from one ormore elements of apparatus 1800. Control component 1840 may include asingle interconnected component and/or two or more disconnectedcomponents; alternatively or additionally, internal watchdog system 1832may include multiple instances of control component 1840, each of which,for instance, may be associated with a corresponding detector component1800.

In an embodiment, and still referring to FIG. 18 , control component1840 may determine, as a result of comparison to a reference value, thatone or more fault conditions has occurred, and/or that one or moresafety limits has been reached. For instance, and without limitation,control component 1840 may compare parameters monitoring one or morepower supplies to reference values representing sufficient power levelsfor proper operation of deterrents and/or one or more elements thereof,where power supplies are not sufficient to ensure proper operation ofscanning or other output elements, a fault condition may be raised andan output limiting element may be activated. As a non-limiting example,where a parameter measuring scanning velocity of a directed lightdeterrent is below some threshold limit such as without limitation 1radian per second, for more than a threshold period of time, anoutput-limiting element of the directed light deterrent may be activateddue to an unacceptably long dwell time of directed light deterrent onone spot. As a further non-limiting example, an instantaneous and/orcumulative measure of intensity, energy, and/or power of a deterrentsuch as a directed light deterrent, acoustic deterrent, and/orelectrical deterrent may be compared to threshold values representingmaximal safe limits for such values.

Further referring to FIG. 18 , and as a non-limiting example, intensityand/or power levels of a directed light deterrent may be set accordingto one or more safety limitations. For instance, and without limitation,intensity and/or duration of light output may be limited to less thanmaximum permissible exposure (MPE) and/or some percentage less than ofMPE, as determined as a function of beam intensity, beam divergence, anddistance from light source of an individual. For instance, where lightsource is non-collimated and diverging, MPE for an individual at anopposite side of subject area may permit a substantially higherintensity than MPE for an individual who is near to light source. Asnoted above, intensity of light source may be varied according to whichan individual is being exposed to light. In an embodiment, a visiblelight source may be configured to shut off automatically where distanceto an individual is less than a preconfigured threshold amount. Distanceto an individual may be determined, without limitation, using time offlight calculation, object inference, stereoscopic vision, and/or other3D measuring techniques. MPE may be calculated, without limitation,according to ANSI4 power exposure safety limitations. MPE levels fromdirected light source may be measured at various power output levelsusing a power meter, to determine how MPE depends on output intensity,distance to an individual, and/or any other parameters, permittingaccurate safety determinations and/or computation of preconfigureddistance threshold for shutoff. Power provided to an individual mayalternatively or additionally be determined using real-time feedback.For instance, power density in a target area such as a vision band of anindividual may be measured using chroma and/or luma values captured at awavelength of directed light deterrent; such feedback may be used toadjust intensity in real time and/or provided to remote device 1848 forupdated safety thresholds and/or firmware updates.

As a further non-limiting example, and continuing to refer to FIG. 18 ,a directed light deterrent may be evaluated for a plurality of criteria,each having a different reference value, for instance as described insafety standards such as without limitation the IEC 60825-1 and the ANSIZ1636.1. Such criteria may include, without limitation, single pulseMPE, multiple pulse MPE, and average power MPE. The terms “single pulse”and “multiple pulse,” as used in this disclosure refer to phenomena thata human eye may perceive due to a scanning action. When a laser beamscans across the pupil of the viewer's eye, it may deliver a pulse oflight to an individual's eye. This is because as the beam scans past theindividual's eye, it will only enter the eye for a brief time, dependingon beam diameter and the scan rate. This perceived pulse of light may becreated by a scanned beam may be similar to a pulse that is created by abeam which is not scanning but is turned on for only a brief instant. Anamount of time that a beam is on within the viewer's pupil may bereferred to as the pulse width. Safety standards may prescribe a maximumamount of light, that is, a maximum permissible exposure (MPE) that anindividual can be receive for a single pulse, and for multiple pulses.

Still referring to FIG. 18 , reference values may be set according toone or more efficacy and/or safety considerations. For instance, athreshold for power emitted by light source may be increased for anindividual detected as having eyewear, a threshold for power emitted byan acoustic deterrent may be increased for an individual wearing earprotection may be increased, or the like. Determination that anindividual is wearing protective equipment may be performed by apparatus1800 and/or by a local and/or remote computing device, for instance andwithout limitation as described in U.S. Provisional Application No.63/067,142, and may be provided to internal watchdog system 1832according to any form of electronic communication as described above.

Further referring to FIG. 18 , where a parameter of first parameter andsecond parameter is a cumulative parameter, control component 1840 maybe configured to reset cumulative parameter upon occurrence of aspecified event. For instance, and without limitation, where a parameterof first parameter and second parameter includes a per-engagement value,control component 1840 may be configured to determine that an engagementhas terminated and reset the per-engagement value. Control component1840 may compare aggregate parameters and/or individual parameters torespective thresholds; in an embodiment, failure of any thresholdcomparison may result in activation of output limiting element.

With continued reference to FIG. 18 , internal watchdog system 1832 maybe configured to transmit at least a sensor output to a processor 1844.Processor 1844 may be included in apparatus 1800 and/or may be includedin and/or include a remote device 1848. Processor 1844 may include anycomputing device as described in this disclosure, including withoutlimitation a microcontroller, microprocessor 1844, digital signalprocessor 1844 (DSP) and/or system on a chip (SoC) as described in thisdisclosure. Processor 1844 may include, be included in, and/orcommunicate with a mobile device such as a mobile telephone orsmartphone. Processor 1844 may include a single computing deviceoperating independently, or may include two or more computing deviceoperating in concert, in parallel, sequentially or the like; two or morecomputing devices may be included together in a single computing deviceor in two or more computing devices. Processor 1844 may interface orcommunicate with one or more additional devices as described below infurther detail via a network interface device. Network interface devicemay be utilized for connecting processor 1844 to one or more of avariety of networks, and one or more devices. Examples of a networkinterface device include, but are not limited to, a network interfacecard (e.g., a mobile network interface card, a LAN card), a modem, andany combination thereof. Examples of a network include, but are notlimited to, a wide area network (e.g., the Internet, an enterprisenetwork), a local area network (e.g., a network associated with anoffice, a building, a campus or other relatively small geographicspace), a telephone network, a data network associated with atelephone/voice provider (e.g., a mobile communications provider dataand/or voice network), a direct connection between two computingdevices, and any combinations thereof. A network may employ a wiredand/or a wireless mode of communication. In general, any networktopology may be used. Information (e.g., data, software etc.) may becommunicated to and/or from a computer and/or a computing device.Processor 1844 may include but is not limited to, for example, acomputing device or cluster of computing devices in a first location anda second computing device or cluster of computing devices in a secondlocation. Processor 1844 may include one or more computing devicesdedicated to data storage, security, distribution of traffic for loadbalancing, and the like. Processor 1844 may distribute one or morecomputing tasks as described below across a plurality of computingdevices of computing device, which may operate in parallel, in series,redundantly, or in any other manner used for distribution of tasks ormemory between computing devices. Processor 1844 may be implementedusing a “shared nothing” architecture in which data is cached at theworker, in an embodiment, this may enable scalability of system and/orcomputing device.

With continued reference to FIG. 18 , processor 1844 may be designedand/or configured to perform any method, method step, or sequence ofmethod steps in any embodiment described in this disclosure and/or thatmay occur to a person skilled in the art having the benefit of thisdisclosure, in any order and with any degree of repetition. Forinstance, processor 1844 may be configured to perform a single step orsequence repeatedly until a desired or commanded outcome is achieved;repetition of a step or a sequence of steps may be performed iterativelyand/or recursively using outputs of previous repetitions as inputs tosubsequent repetitions, aggregating inputs and/or outputs of repetitionsto produce an aggregate result, reduction or decrement of one or morevariables such as global variables, and/or division of a largerprocessing task into a set of iteratively addressed smaller processingtasks. Processor 1844 may perform any step or sequence of steps asdescribed in this disclosure in parallel, such as simultaneously and/orsubstantially simultaneously performing a step two or more times usingtwo or more parallel threads, processor 1844 cores, or the like;division of tasks between parallel threads and/or processes may beperformed according to any protocol suitable for division of tasksbetween iterations. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various ways in whichsteps, sequences of steps, processing tasks, and/or data may besubdivided, shared, or otherwise dealt with using iteration, recursion,and/or parallel processing. In an embodiment, processor 1844 may operatedeterrent components and/or determine deterrent output selections, forinstance and without limitation as described in U.S. ProvisionalApplication No. 63/067,142.

In an embodiment, and still referring to FIG. 18 , receiving a referencevalue may include receiving the reference value as a function of atransmitted sensor output. For instance, control component 1840,apparatus 1800, and/or any element of apparatus 1800 and/or internalwatchdog system 1832 may transmit one or more detected parameters, asdescribed above, to a processor 1844 as described above. Transmissionmay be performed continuously and/or periodically, and/or may beperformed in response to events such as interactions with an individual.As a non-limiting example, apparatus 1800 and/or components thereof mayreceive cumulative values from other devices such as apparatuses 1800and/or processors 1844, may add to and/or otherwise modify suchcumulative values, and/or may transmit cumulative values to otherdevices. Apparatus 1800 and/or one or more components thereof maytransmit and/or receive notifications that an interaction with anindividual has occurred; such notifications may, e.g., be used ininitiation and/or reset of cumulative parameters, in updates tocumulative parameters, or the like. Control component 1840 may beconfigured to transmit an indication of output reduction elementactivation to a processor 1844 or other device. Control component 1840may be configured to receive an indication of output reduction elementactivation from another device; control component 1840 may activate oneor more output reduction elements in turn. As non-limiting example,control component 1840 may receive an identification of an outputreduction element activated on another device and activate acorresponding output reduction element on apparatus 1800; asnon-limiting example, where the other device indicates that a directedlight deterrent output reduction element such as an optical modulatorand/or shutter has been activated, control component 1840 may activate acorresponding output reduction element, such as an optical modulatorand/or shutter, of apparatus 1800.

Referring now to FIG. 19 , a block diagram of an exemplary embodiment ofa control component 1840 including one or more elements of analogcircuitry is illustrated. A control signal controlling activation of anoutput reduction element 1904, which may include any element orcomponent suitable for use as first output reduction element 1824 and/orsecond output reduction element 1828 as described above, may be outputby an analog element or circuit configured to compare at least areference parameter to a parameter received from detector component1800. For instance, and without limitation, a comparison between adetector component 1800 parameter and a reference parameter may beperformed, at least in part, using a comparator 1908. Alternative oradditional elements in a comparison circuit may include diodes,transistors, or other elements; for instance, a difference betweenreference parameter and detector parameter may be used directly as anactivation signal for an output reduction element 1904. Referenceparameter may include any electrical parameter and may include the sametype of electrical parameter as detector parameter or may include adistinct parameter therefrom. For instance, and without limitation,reference parameter may include a reference voltage, for instance asgenerated across a resistor, resistive divider, transistor, any variableresistor as described above, or the like. Reference parameter may bereceived and/or generated in any manner described above in reference toFIG. 18 , including without limitation from digital components asdescribed in further detail below. Any detector parameter may similarlyinclude or be converted to voltage for use with a comparator 1908 orother comparison circuit. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various alternative oradditional analog circuits, elements, and/or components that may be usedto implement a comparison circuit as defined above, each of which iscontemplated as within the scope of this application.

Still referring to FIG. 19 , there may be various comparison circuitsused to compare multiple parameters to multiple thresholds. Forinstance, and without limitation, where a plurality of parameters arecombined to control a single output reduction component 1904, comparisoncircuits may be stacked and/or nested so that the output of onecomparison circuit functions as a detector parameter and/or referenceparameter for a subsequent or downstream comparison circuit. Multipledetector parameters may alternatively or additionally be combined usingupstream circuit elements prior to input to comparison circuits, and/oroutputs of multiple comparison circuits may be combined using anycombination of analog elements, including without limitation any analogelements for aggregation or other mathematical operations as describedbelow. Alternatively or additionally, outputs of comparison circuits maybe fed to digital elements, for instance as described below, forcombination or additional analysis.

With continued reference to FIG. 19 , analog elements of controlcomponent 1840 may include elements used for calculation using,combination of, and/or processing of reference parameters and/ordetector parameters. Such elements may include, without limitation, oneor more amplifier circuits 1908. For instance, and without limitation,aggregate parameters may be generated in amplifier circuit 1908 usingoperational amplifier-based adders and/or multipliers. As a furthernon-limiting example, cumulative parameters may be calculated and/orgenerated using capacitors, inductors, and/or cumulative amplifiercircuit elements such as without limitation integrators. Scanningvelocities and/or other rates of change of measurable quantities may bedetermined, without limitation, using operational amplifierdifferentiators. Persons skilled in the art, upon reviewing the entiretyof this disclosure, will be aware of various analog and/oramplifier-based circuit elements and/or combinations thereof that may beused to generate detector parameters, reference parameters, or the like.Such calculation elements may alternatively or additionally be usedbetween and/or within stages of comparison circuits.

Referring now to FIG. 20 , a block diagram of exemplary embodiments ofdigital circuit elements 2000 of a control component 1840 isillustrated. Digital circuit elements 2000 may be implemented as anASIC, FPGA, microprocessor 1844, microcontroller, or the like. In someembodiments, digital circuit elements 2000 may be reprogrammed and/orupdated using one or more software and/or firmware updates, which may bereceived without limitation, from a remote device 1848 and/or processor1844. Digital circuit elements 2000 may include, without limitation, oneor more logic gates and/or modules to compare, calculate, aggregate,and/or arrive at logical determinations concerning detector parameters,reference parameters and the like. One or more calculations and/ordeterminations may be performed using an arithmetic and logic unit (ALU)2004, and/or any component and/or components suitable for use therein,such as without limitation adders, multipliers, logical OR, AND, NOR,NAND, XOR, or NOT operators, shift operators, comparison and/orsubtraction operators, or the like, which may be augmented with zeroflags, overflow bits, or the like. Digital circuit elements may includeone or more registers 2008 and/or other elements used to store andretrieve data such as binary data. Digital circuit elements may includeone or more counters 2012 for incrementing values, counting clock cyclesto work out timing signals, or the like. Digital circuit elements 2000may include at least a digital signal processing (DSP) module 2016,which may perform signal analysis tasks such as convolution, FastFourier Transform (FFT), digital filtering, and/or other processes forsignal processing and/or manipulation, for instance and withoutlimitation with regard to detector parameter signals and/or referenceparameter signals. Digital circuit elements 2000 may include one or moredriver elements 2020, which may generate signals to drive elements to beoperated by digital circuit elements 2000, such as without limitationoutput limiting elements 1904.

Still referring to FIG. 20 , detector parameters may be input directlyto digital circuit elements 2000 where detector components 1800 aredigital and/or output a signal matching a ‘1’ or ‘0’ voltage level,either directly or by way of an amplifier, voltage divider, Zener diode,or other circuit element driving or limiting outputs to particularvoltages, and which is capable of conveying meaning by way of itsvoltage level, such as an “on/off” detection signal, a pulse widthmodulated (PMW) signal, or the like. Alternatively or additionally, asignal from a detector component 1800 may be converted to a digitalsignal using an analog to digital (A/D) converter 2024, which mayencode, for instance, voltage levels of an incoming voltage signal asdigitally encoded binary numerical numbers or the like. Digital circuitelements 2000 may have one or more hardware and/or software modulesconfigured to interpret inputs from an A/D converter 2024 and/ordetector component 1800 and perform comparisons, analysis, aggregation,accumulation, or the like of such signals.

With further reference to FIG. 20 , digital circuit elements 2000 mayperform output of signals, for instance and without limitation for,controlling and/or driving output limiting elements 1904. In someembodiments, such as when output limiting elements are digitallycontrolled and/or controllable by patterns of ‘0’ and ‘1’ digitalvalues, such as via PWM signals or the like, digital outputs may be useddirectly or via one or more circuit elements such as amplifiers or thelike to control and/or communicate with downstream components.Alternatively or additionally, a signal output by digital circuitelements 2000 may be converted to an appropriate control signal by oneor more analog elements such as a low-pass filter, which may convert PWMsignals to voltage control levels or the like. In a further alternativeor additional embodiment, digital signals may be converted to analogcontrol signals using a digital to analog (D/A) converter 2028.

Still referring to FIG. 20 , digital circuit elements 2000 may beconfigured to communicate with processor 1844 and/or a remote device1848. For instance, and as described in further detail above inreference to FIG. 18 , reference values may be received from processor1844 and/or remote device 1848. As a further non-limiting example, datasuch as an indication that an output reduction element 1904 has beenactivated, data indicating a current value of a cumulative parameter,and/or interaction data regarding an individual may be transmitted toprocessor 1844 and/or to a remote device 1848.

Continuing to refer to FIG. 20 , some elements of control component 1840may be implemented using analog circuitry while other elements may beimplanted using digital circuitry, for instance, as dictated withoutlimitation by performance, reliability, complexity, efficient use ofpower, and/or any other design considerations. Similarly, and asillustrated in part in FIGS. 2 and 3 , analog elements may input toand/or receive outputs from digital elements, and digital elements mayinput to and/or receive output from analog elements.

In some embodiments, internal watchdog system 1832 may function as aninterlock safety system as described in U.S. Provisional Application No.63/067,142, acting to provide a redundant and fail-safe protectivemeasure in combination with other processes performed in a deterrentapparatus.

Referring now to FIG. 21 , an exemplary embodiment of an autonomoussafety system 2100 for a deterrent 2112 apparatus is illustrated. System2100 includes a processor 2104, which may include any processor 2104 asdescribed in this disclosure. Processor 2104 may be incorporated in anapparatus and/or may be incorporated in a separately housed device whichmay be a part of a mesh network, communicatively connected to one ormore elements of an apparatus as described above, or the like.

With continued reference to FIG. 21 , processor 2104 may be designedand/or configured to perform any method, method step, or sequence ofmethod steps in any embodiment described in this disclosure, in anyorder and with any degree of repetition. For instance, processor 2104may be configured to perform a single step or sequence repeatedly untila desired or commanded outcome is achieved; repetition of a step or asequence of steps may be performed iteratively and/or recursively usingoutputs of previous repetitions as inputs to subsequent repetitions,aggregating inputs and/or outputs of repetitions to produce an aggregateresult, reduction or decrement of one or more variables such as globalvariables, and/or division of a larger processing task into a set ofiteratively addressed smaller processing tasks. Processor 2104 mayperform any step or sequence of steps as described in this disclosure inparallel, such as simultaneously and/or substantially simultaneouslyperforming a step two or more times using two or more parallel threads,processor 2104 cores, or the like; division of tasks between parallelthreads and/or processes may be performed according to any protocolsuitable for division of tasks between iterations. Persons skilled inthe art, upon reviewing the entirety of this disclosure, will be awareof various ways in which steps, sequences of steps, processing tasks,and/or data may be subdivided, shared, or otherwise dealt with usingiteration, recursion, and/or parallel processing.

In an embodiment, and still referring to FIG. 21 , processor 2104 isconfigured to detect, using at least a detection component 2108communicatively connected to at least a deterrent 2112 of a deterrent2112 apparatus 100, at least a deterrent parameter 2116. A “detectioncomponent 2108,” as used herein, is a sensor, meter, or other devicethat can detect a deterrent parameter 2116. At least a detectioncomponent 2108 may include one or more sensors or other components formeasuring parameters 2116 as described in further detail below;alternatively or additionally, such sensors and/or components may beintegrated in an apparatus 100. A “deterrent parameter 2116,” as used inthis disclosure, is any measurable electrical, energy, or other aspectof a deterrent output. A “deterrent output” is defined of the purposesof this disclosure as a physical or psychological interaction with anindividual that discourages and/or stops the individual from performinga behavior contrary to one or more security objectives; the one or moresecurity objectives may be determined by apparatus and/or enteredthereon by a user, for instance and without limitation as describedabove. A “deterrent” for purposes of this disclosure, is a component orelement of an apparatus that outputs a deterrent output. A deterrent2112 may include additional elements as described in further detailbelow.

Further referring to FIG. 21 , detection component 2108 may include acomponent configured to detect parameters 2116 of a particular form ofdeterrent 2112. For instance, and without limitation, where a deterrent2112 includes a directed light deterrent 2112, a detected parameter 2116may include irradiance generated by the directed light deterrent 2112. A“directed light deterrent 2112,” as used in this disclosure, is adeterrent 2112 that uses a high-intensity light source, such as, but notlimited to, a laser, super LED, laser illuminated LED, super-luminescentLED, EELD, VCSEL, plasma discharge lamp, and/or high-intensity LED thatis actively aimed at and/or focused on an individual to be deterred, togenerate a deterrent 2112 effect. A directed light deterrent 2112 mayinclude a beam steering component, which may include, withoutlimitation, two or more reflective elements used as scanning mirrors,spatial light modulators, metamaterials/metasurfaces, liquid crystaldirectors, Risley prisms, microoptical arrays, fast steering mirrors,tip/tilt optics, holographic phase modulators, and/or off-centered lenselements. In one embodiment, reflective elements, which may include anyreflective elements for use in scanning mirrors as described above inreference to light radar component, may be arranged in close proximityto one another on axes that are substantially orthogonal causing onemirror to act as a vertical scanning mirror and another mirror to act asa horizontal scanning mirror. Such an arrangement may enable rapidscanning of laser and or other light beams across objects in subjectarea. Directed light deterrent 2112 may include any additional opticssuitable for use in optical instruments such as lasers or other highintensity light sources, including additional amplifiers, beamexpanders, or the like. In an embodiment, a beam may be collimated, ormay not be collimated at one or more different stages in its processingby optical instruments within directed light deterrent 2112. Light fromdirected light deterrent 2112 may be coherent or may not be coherent,depending on desired applications. In some embodiments, optical elementsthrough which a beam may pass in directed light deterrent 2112 may havean effect of dissipating, polarizing, wavelength shifting, filtering,modifying, homogenizing, interrupting, or spreading power of the beam.As a result, a beam incident on objects in subject area, includingwithout limitation a face or eyes of an individual, may havesubstantially lower intensity than at initial production of beam.

With continued reference to FIG. 21 , detection component 2108 mayinclude an optical power meter for measuring a degree of lightintensity. Optical power meter may include, without limitation, ameasuring head such as model number SEL033 and an optical amplifierboard such as model A430, both from the company International Light. Anoptical power meter may be used to constantly monitor the power of alaser beam or other beam being emitted from a directed light deterrent2112. Optical power meter may detect power of a mean using a pick-offand/or beam splitter-based optical power meter or may include apass-through optical power meter. Alternatively or additionally, adetection component 2108 measuring power and/or irradiance of light froma directed light deterrent 2112 may include a camera configured tocapture light incident on an individual and/or object, which captureddata may be used to calculate and/or estimate incident intensity. A“camera,” as used in this disclosure, is an imaging device, which mayinclude any imaging device or combination of imaging devices asdescribed above.

As a further example, and continuing to refer to FIG. 21 , where seconddeterrent 2112 includes an audio deterrent 2112, second parameter 2116may include a measure of acoustic intensity, such as a level of acousticintensity represented using decibels safety with regard to humanhearing. An audio deterrent 2112 may include, without limitation, anyaudio deterrent 2112 as described above. For instance, and withoutlimitation, audio deterrent 2112 may broadcast sound or may aim sound ina given direction using, for instance, a sound reflecting device such asa parabolic reflector or the like. An audio deterrent 2112 may include adirected sound source, for instance as defined above.

With continued reference to FIG. 21 , at least one of the firstparameter 2116 and the second parameter 2116 may include an electricalparameter 2116. An “electrical parameter 2116,” as used in thisdisclosure, is any parameter 2116 that may be directly or indirectlymeasured with regard to an electrical circuit. For instance, and withoutlimitation, an electrical parameter 2116 may include a voltage level. Asa further non-limiting example, an electrical parameter 2116 may includea current. Other electrical parameters 2116 may include, withoutlimitation, capacitance, inductance, resistance, or the like. Electricalparameters 2116 may include voltage, current, and/or power to particulardeterrents 2112. For instance, voltage, current, and/or electrical powerto a deterrent 2112 that converts electrical power to a proportionallyrelated output, such as a directed light deterrent 2112, an audiodeterrent 2112, an electrical deterrent 2112, or the like, may be usedto represent power being output by such deterrents 2112; values soderived may be used as substitutes for and/or in addition to devicesthat directly measure output such as meters of light intensity, soundintensity, or the like.

Still referring to FIG. 21 , a detection component 2108 measuring anelectrical parameter 2116 may include a power supply detection component2108. A power supply detection component 2108 may monitor the powersupply voltages that feed one or more deterrents 2112 and/or an outputfrom a power supply component such as a connection to alternatingcurrent mains power, an energy storage device such as a battery or fuelcell, a photovoltaic power source, or the like; power drawn from a powersource may be compared to power drawn by components, for instance andwithout limitation to detect faults in apparatus and/or detector elementcircuitry.

With continue reference to FIG. 21 , a detection component 2108 maymeasure an electrical parameter 2116 of another detection component2108. For example, and without limitation, where a first detectioncomponent 2108 measures intensity of a deterrent output, a seconddetection component 2108 may measure one or more electrical parameters2116 of the first detection component 2108 to determine whether thefirst detection component 2108 is functioning effectively; if firstdetection component 2108 is not functioning correctly, an outputreduction element 2140 corresponding to the deterrent 2112 may beactivated to prevent potential safety issues. As a non-limiting example,a beam power meter monitor may measure an output of a beam power meteras described above, and make sure that it is functioning properly.

Still referring to FIG. 21 , a parameter 2116 may include a temperature.Temperature may be measured, without limitation, using an electricalcomponent for which at least one electrical parameter 2116 changes as aresult of a change in temperature, such as without limitation athermistor or the like. Where detection component 2108 is remote fromapparatus, temperature may be measured indirectly using, withoutlimitation, an infrared camera, or the like. In an embodiment, atemperature of a circuit element may be used to indicate likelyperformance of that element; for instance, semiconductor elements suchas transistors, diodes, LEDs, or the like may function differently whenoverheated then when operating at ordinary temperatures. As a furthernon-limiting example, a temperature of a deterrent 2112 may indicatepower output and/or may be compared to electrical power input to thedeterrent 2112 calculate power output by measuring waste heat generatedby the deterrent 2112. As an additional non-limiting example, atemperature of a particular component above a preconfigured thresholdmay indicate that the component is about to fail or is likely to behavein an unpredictable or inefficient manner.

With continued reference to FIG. 21 , a deterrent parameter 2116 mayinclude a cumulative energy value. A “cumulative energy value,” as usedin this disclosure, is a value representing a total amount of energydelivered by a deterrent 2112 over a given time period. For instance,and without limitation, maximum permissible exposure for a directedlight deterrent 2112 as described in further detail below may specify amaximum instantaneous intensity, irradiance, and/or power delivery,and/or may specify a maximum total energy which may be delivered to theeyes of an individual, where the latter may be compared to a cumulativeenergy value representing a total quantity of energy delivered by adirected light deterrent 2112. As an additional non-limiting example,electrical deterrents 2112 may have both instantaneous limits on voltageand/or current as well as overall electrical power delivery limits, thelatter of which may correspond to a cumulative energy value representinga total electrical energy delivered. As a further non-limiting example,an acoustic deterrent 2112 may be subject to a maximum instantaneousmeasure of intensity such as a decibel level, as well as a cumulativelimit of decibels above a threshold level that are delivered over agiven time period. A cumulative energy value may depend both on time andintensity of exposure; that is, exposure beneath a threshold of power orintensity may not be added to the cumulative energy value.

In an embodiment, and still referring to FIG. 21 , a cumulative energyvalue may include a per-engagement value. A per-engagement value, asused in this disclosure, is a cumulative energy value that accumulatesover the course of an engagement. An “engagement,” as used herein, is asingle interaction or sequence of interactions of a particularindividual with apparatus or a network or system containing apparatus.For instance, an engagement may include a period of time during which anindividual is attempting to access a forbidden or protected zone, istrespassing on a premises being protected by apparatus, and/or isotherwise engaging in behaviors that cause apparatus to use deterrents2112 against the individual. An engagement may include an uninterruptedperiod in which an individual is in a subject area protected byapparatus and/or a system including apparatus and/or may include aseries of such uninterrupted periods that are temporally proximate. Forinstance, apparatus and/or a component thereof may include a variablethat is set when interaction with individuals and/or a particularindividual begin, indicating initiation of an engagement, and may becleared and/or reset, indicating the end of an engagement, wheninteraction generally or with a specific individual has ceased for athreshold period of time. For instance, termination of an engagement maybe recorded when a given person has been absent from and/or notinteracting with apparatus for one hour, one day, or any other suitableperiod. In an embodiment, each variable may receive initiation and resetsignals, and/or signals identifying a particular individual as currentlyinteracting, from apparatus, permitting use of facial recognition and/orother data determined by apparatus to be used in determining whether agiven individual is currently interacting with is currently adding to acumulative energy value while a corresponding deterrent 2112 isoutputting. In such an exemplary embodiment, where apparatus is notidentifying particular individuals, all interactions may be treated ascorresponding to a single individual; that is, cumulative energy valuesmay depend solely on outputs generated during an engagement as delimitedby cessation of all interaction for a threshold period of time.Individual-agnostic cumulative energy values and/or per-engagementenergy values may alternatively or additionally be used as a fail-safelimit to prevent accidentally exceeding cumulative energy values due tofaulty recognition of distinct individuals.

With further reference to FIG. 21 , cumulative values may be tracked andrecorded across a mesh network. Mesh networks may be used to coordinateresponses between two or more apparatuses. For instance, two apparatusesin the same subject area may coordinate transmission of directed lightdeterrent 2112 actions, or other actions based upon detected user anindividual behavior, postures, or the like. For instance, and withoutlimitation, two or more apparatuses may have two or more deterrent 2112light wavelengths which may be deployed concurrently or sequentially inorder to add to confusion and/or resistance to eyewear protection asdescribed above. Alternatively or additionally, two or more apparatusesdeployed in two or more separate security zones and/or subject areas maycoordinate by communicating actions and/or determinations concerningentrance and/or intrusions in such security areas. This may be used, forinstance, to determine what ambient light exposure an individual hasexperienced, which direction an individual has come from, and/or whatactivity an individual may be expected to perform. For instance, whereone apparatus has detected aggressive behavior by an individual, thismay be used as an immediate blacklist by other apparatuses, where anindividual identified as the same an individual entering a new subjectarea may be immediately responded to with more aggressive responses suchas saturation, strobing, electric shock or other responses, on the basisthat this an individual has been identified as a threat that must beneutralized. Such data may also be transmitted remotely, and sent asupdates to security teams, law enforcement, or other users attempting torespond to an ongoing or developing security threat. Such user may usesuch information to determine a likely current location of a perpetratorand or other an individual as well as to formulate or plan a strategyfor counteracting the actions of an individual and neutralizing anythreat. Two or more apparatuses deployed in the same area may be used tocreate one or more additional coordinated actions, such as creation oflight curtains, to indicate divisions between authorized andunauthorized areas, guide crowd movement, or the light. As a furtherexample, a series of apparatus is may provide directional indicatorssuch as directional images or the like which made direct entrance andour users in Direction Evacuation, or the like. In an embodiment,tracking and incrementing of cumulative energy values across meshnetworks may help to ensure that apparatuses across a mesh network donot combine to exceed cumulative safety limits.

With continued reference to FIG. 21 , at least one of first parameter2116 and second parameter 2116 may include a location-dependentparameter 2116. A “location-dependent parameter 2116,” as used in thisdisclosure, is a cumulative parameter 2116 that applies for some regionof an area or volume that may be scanned by a deterrent 2112 such aswithout limitation first deterrent 2112 or second deterrent 2112. Forinstance, a region across which a deterrent 2112 may scan may be dividedinto sectors, identifiable by processor 2104 by measurement of scanningx and y values, for instance as described above; a total plane and/orvolume of scanning may be divided into sectors, and cumulative valuesmay be taken per-sector. Alternatively or additionally, a deterrent 2112apparatus may provide a current sector containing an individual toprocessor 2104, and cumulative energy values for that individual may berecorded per the indicated sector.

Still referring to FIG. 21 , at least one of first parameter 2116 andsecond parameter 2116 may include a distance-dependent parameter 2116. A“distance-dependent parameter,” as used in this disclosure, is aparameter 2116 that depends on a distance from an individual toapparatus. For instance, and without limitation, an individual 10 feet(3 m) away from a directed light deterrent 2112 may receive a higheramount of optical energy than individual 20 feet (6 m) away from thedirected light deterrent 2112, per output irradiance, owing to beamdispersal, diffraction, and the like. Apparatus may provide processor2104 with a value indicating a current distance from apparatus of anindividual. Processor 2104 may alternatively or additionally measureand/or estimate such distances using any distance measurement techniquesand/or technologies as described above, including ToF and/or imageanalysis. Processor 2104 may weight measurements taken for cumulativeand/or instantaneous values according to value.

With continued reference to FIG. 21 , at least one of first parameter2116 and second parameter 2116 may include an aggregate parameter 2116.An “aggregate parameter,” as used in this disclosure, is a parameter2116 calculated as a function of two or more parameters 2116 asdescribed above. Aggregation may include addition, averaging, or anyother form of combination of values. As a non-limiting example, aplurality of parameters 2116 regarding one deterrent 2112 may beaggregated together to generate an aggregate parameter 2116 which may beused as an alternative or additional parameter 2116 to the individualparameters 2116. For instance, scanning velocity, output intensity,and/or other parameters 2116 corresponding to a directed light deterrent2112 may be combined to generate an aggregate parameter 2116 which maybe measured in its own right.

Still referring to FIG. 21 , at least a deterrent parameter 2116 may bereceived directly from detection component 2108. Alternatively oradditionally, at least a deterrent parameter 2116 may be derived usingone or more machine-learning methods and/or models. For instance, andwithout limitation, a deterrent parameter 2116 machine-learning model2120 may input one or more elements of data and/or parameters 2116 fromone or more detection components 2108 and output a parameter 2116,aggregate parameter 2116, or the like. As a further example, deterrentparameter 2116 machine-learning model 2120 may input one or moremeasures of distance, location, or the like to determine adistance-related or location-based parameter 2116 as described above.For instance, and without limitation a machine-learning model may inputone or more elements of data and/or parameters 2116 from one or moredetection components 2108 and output a parameter 2116, aggregateparameter 2116, or the like. As a further example, a machine-learningmodel may input one or more measures of distance, location, or the liketo determine a distance-related or location-based parameter 2116 asdescribed above. Machine-learning model may be trained, withoutlimitation, using any machine-learning process and/or algorithm asdescribed above, and using any training data, as described above,correlating desired inputs of deterrent parameter 2116 machine-learningmodel 2120 to desired outputs thereof. Deterrent parameter 2116machine-learning model 2120 may be trained by processor 2104, and/or ona remote device which may provide deterrent parameter 2116machine-learning model 2120

Still referring to FIG. 21 , processor 2104 is configured to compare theat least a deterrent parameter 2116 to a safety threshold 2124. A“safety threshold 2124,” as described herein, is a quantitative datum orcollection of data representing a maximal or minimal value consistentwith safe operation of an apparatus and/or a deterrent 2112 included inan apparatus, for one or more parameters 2116. Safety threshold 2124 mayinclude a single numerical value, a vector or n-tuple of numericalvalues, and/or any other suitable representation.

With continued reference to FIG. 21 , safety threshold 2124 may beretrieved from a threshold database 2128. Threshold database 2128 may beimplemented, without limitation, as a relational threshold database2128, a key-value retrieval threshold database 2128 such as a NOSQLthreshold database 2128, or any other format or structure for use as athreshold database 2128 that a person skilled in the art would recognizeas suitable upon review of the entirety of this disclosure. Thresholddatabase 2128 may alternatively or additionally be implemented using adistributed data storage protocol and/or data structure, such as adistributed hash table or the like. Threshold database 2128 may includea plurality of data entries and/or records as described above. Dataentries in a threshold database 2128 may be flagged with or linked toone or more additional elements of information, which may be reflectedin data entry cells and/or in linked tables such as tables related byone or more indices in a relational threshold database 2128. Personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various ways in which data entries in a threshold database2128 may store, retrieve, organize, and/or reflect data and/or recordsas used herein, as well as categories and/or populations of dataconsistently with this disclosure. Retrieval from threshold database2128 may be performed, without limitation, by constructing a query, andusing the query-to-query threshold database 2128. Query may include,without limitation, a type of deterrent 2112, a type of parameter 2116,one or more elements of data describing individual, or any other datathat may be available to processor 2104 using any apparatus and/orcomponent, and/or communication therewith, as described in thisdisclosure.

Alternatively or additionally, and still referring to FIG. 21 ,threshold comparison may be performed using threshold machine-learningmodel and/or threshold inference system 2132. Inference system mayinclude a fuzzy inference system, which may include any system matchingparameters 2116 to membership functions, where “membership functions”describe one or more values for a linguistic variable such as“intensity” or “power”; one or more membership functions may be centeredaround numerical values along a spectrum, and may include a calculationestablishing a probability of matching a given value of an input fallingalong the spectrum. Fuzzy inference system may further include one ormore output functions, parameters 2116, and/or membership functions.Output parameters 2116 functions, parameters 2116, and/or membershipfunctions may include coefficients and/or other parametric values whichmay be calculated, trained, and/or tuned using any machine-learningmethod and/or process as described above. Training examples used to tunethreshold inference system 2132 may include any form of training data asdescribed above and may be recorded using, as a non-limiting example,sensors mounted to an individual, other sensors having greater accuracy,and/or taken under one or more controlled circumstances, relating atleast a deterrent parameter 2116 to one or more values associated withsafety standards, for instance as described above.

As a non-limiting example, and further referring to FIG. 21 intensityand/or duration of light output may be limited to MPE and/or somepercentage less than of MPE, as determined as a function of beamintensity, beam divergence, and distance from light source of anindividual. For instance, where light source is non-collimated anddiverging, MPE for an individual at an opposite side of subject area maypermit a substantially higher intensity than MPE for an individual whois near to light source. As noted above, intensity of light source maybe varied according to which an individual is being exposed to light. Inan embodiment, a visible light source may be configured to shut offautomatically where distance to an individual is less than apreconfigured threshold amount. Distance to an individual may bedetermined, without limitation, using time of flight calculation, objectinference, stereoscopic vision, and/or other 3D measuring techniques.MPE may be calculated, without limitation, according to ANSI4 powerexposure safety limitations. MPE levels from directed light source maybe measured at various power output levels using a power meter, todetermine how MPE depends on output intensity, distance to anindividual, and/or any other parameters 2116, permitting accurate safetydeterminations and/or computation of preconfigured distance thresholdfor shutoff. Power provided to an individual may alternatively oradditionally be determined using real-time feedback. For instance, powerdensity in a target area such as a vision band of an individual may bemeasured using chroma and/or luma values captured at a wavelength ofdirected light deterrent 2112; such feedback may be used to adjustintensity in real time and/or provided to a remote device for updatedsafety thresholds 2124 and/or firmware updates.

As a further non-limiting example, and continuing to refer to FIG. 21 ,a directed light deterrent 2112 may be evaluated for a plurality ofcriteria, each having a different threshold, for instance as describedin safety standards such as without limitation the IEC 60825-1 and theANSI Z136.1. Such criteria may include, without limitation, single pulseMPE, multiple pulse MPE, and average power MPE. The terms “single pulse”and “multiple pulse,” as used in this disclosure refer to phenomena thata human eye may perceive due to a scanning action. When a laser beamscans across the pupil of the viewer's eye, it may deliver a pulse oflight to an individual's eye. This is because as the beam scans past theindividual's eye, it will only enter the eye for a brief time, dependingon beam diameter and the scan rate. This perceived pulse of light may becreated by a scanned beam may be similar to a pulse that is created by abeam which is not scanning but is turned on for only a brief instant. Anamount of time that a beam is on within the viewer's pupil may bereferred to as the pulse width. Safety standards may prescribe a maximumamount of light, that is, a maximum permissible exposure (MPE) that anindividual can be receive for a single pulse, and for multiple pulses.

Still referring to FIG. 21 , thresholds may be set according to one ormore efficacy and/or safety considerations. For instance, a thresholdfor power emitted by light source may be increased for an individualdetected as having eyewear, a threshold for power emitted by an acousticdeterrent 2112 may be increased for an individual wearing ear protectionmay be increased, or the like. Determination that an individual iswearing protective equipment may be performed by apparatus 100 and/or bya local and/or remote computing device, for instance and withoutlimitation as described above.

Further referring to FIG. 21 , where a parameter 2116 of first parameter2116 and second parameter 2116 is a cumulative parameter 2116, processor2104 may be configured to reset cumulative parameter 2116 uponoccurrence of a specified event. For instance, and without limitation,where a parameter 2116 of first parameter 2116 and second parameter 2116includes a per-engagement value, processor 2104 may be configured todetermine that an engagement has terminated and reset the per-engagementvalue. Processor 2104 may compare aggregate parameters 2116 and/orindividual parameters 2116 to respective thresholds; in an embodiment,failure of any threshold comparison may result in activation of outputlimiting element.

Still referring to FIG. 21 , processor 2104 is configured to determine acorrective action as a function of the comparison. Corrective action maybe programmed using hardware or software programming, retrieved from adatabase, may be determined using one or more machine-learning models,fuzzy inference systems as described above, or the like. For instance,and without limitation, corrective action may be retrieved from acorrective action database 2136, which may include any database suitablefor use as threshold database 2128. Alternatively or additionally,corrective action may be generated using an inference system asdescribed above; for instance, and without limitation, thresholdinference system 2132 may directly output corrective actions and/or dataindicating corrective actions to be performed. Training examples maycombine any training example data described above, correlated withcorrective actions to be performed for achievement of results consideredoptimal by, for instance, an expert, a user deploying apparatus 100, orthe like. Optimal results may include results that reduce and/or modifyone or more deterrent outputs to cease safety violations.

With further reference to FIG. 21 , a corrective action may includetransmission of a message and/or signal to a user, such as a user havingcontrol of one or more apparatuses and/or a “human in the loop” of anysystem as described in this disclosure. Corrective action may includetransmitting a report such as a “bug report” and/or any data concerningfailure to a server and/or other computing device, which may generatenew parameters 2116, software updates, firmware updates, or the likegoverning deterrents 2112 to prevent future safety violations; updatesmay update circuitry and/or computing device elements of apparatus forsetting deterrent 2112 levels based on behavior and/or threat level,and/or may modify thresholds or other comparison parameters 2116 used byone or more internal watchdog systems of apparatus 100 to disabledeterrents 2112 when safety limits are reached.

Alternatively or additionally, and still referring to FIG. 21 ,corrective action may include activation of an output reduction element2140 within apparatus. An “output reduction element,” as used in thisdisclosure, is a component and/or element of a deterrent 2112 componentthat, when activated, attenuates, interrupts, and/or terminates outputby a deterrent 2112 of the deterrent 2112 component. An output reductionelement 2140 may provide enhanced safety characteristics for deterrent2112 suite and/or apparatus as a backup and/or failsafe deterrent 2112interruption based on operating parameters 2116 of the apparatus. Forinstance, and without limitation, where a deterrent 2112 of a deterrent2112 component is a directed light deterrent 2112, an output reductionelement 2140 may include a shutter or similar device that interrupts theoutputted light source. For instance, and without limitation, a shuttermay include an LST400 from NM Laser Products; in an embodiment, ashutter may be able to completely prevent all or substantially all lightoutput within 20 milliseconds of command. A shutter may prevents passageof a beam of light such as a laser beam in the absence of a commandsignal directing the shutter to remain open, such that a fault and/orinterruption in communication with shutter may cause an automaticclosure thereof. As a further example, where first deterrent 2112component includes a directed light deterrent 2112, first outputreduction element 2140 may include an optical modulator, such as withoutlimitation an Acousto-optic modulator or electro-optic modulator such asthose used in q-switching or the like. For instance, and withoutlimitation, an optical modulator may include a polychromaticacousto-optic modulator. Additional non-limiting examples of outputreduction elements 2140 that may be used with a directed light deterrent2112 include filters, attenuators, physical interruptions, or the like

As a further non-limiting example, and continuing to refer to FIG. 21 ,at least one of first output reduction element 2140 and second outputreduction element 2140 may include a power regulation control element. A“power regulation control element,” as used in this disclosure, is anelement activation of which restricts electrical power to a componentsuch as a deterrent 2112 as described above. A power regulation controlelement may include one or more power switches, current and/or voltagelimiters, or the like. Power regulation control elements may beimplanted using physically actuated relays, transistors such as bipolarjunction transistors (BJTs), insulated gate bipolar transistors (IGBTs),field effect transistors such as metal oxide field effect transistors(MOSFETs), thyristors such as integrated gate-commutated thyristors(IGCTs) or triodes for alternating current (TRIACs), variable resistorssuch as photoresistors, thermistors, potentiometers, or the like, and/orany other device that may be used to switch on or off or otherwiseregulate electric current.

Further referring to FIG. 21 , where one of first deterrent 2112 andsecond deterrent 2112 includes an audio deterrent 2112, at least one offirst output reduction element 2140 and second output reduction element2140 may include a sound-attenuating element. A “sound-attenuatingelement,” as used in this disclosure, is an element that physicallyinterferes with emission of sound by an audio output device. Asound-attenuating element may include, without limitation, a door,blanket, or pad that can be closed over a speaker or other audio outputdevice, and/or an object that can be brought into contact with amembrane and/or piezoelectric element of a speaker, and/or a mechanicalmechanism that can move a piezoelectric element out of contact with amembrane or other amplifying medium.

Still referring to FIG. 21 , corrective action may include deactivationof apparatus and/or a deterrent 2112 until a firmware, software update,replacement part, or the like has gone through to fix the issue. In anembodiment, deterrents 2112 that have not been deactivated may still beused by apparatus 100 in the interim.

Further referring to FIG. 21 , processor 2104 is configured to initiatecorrective action. As used herein, “initiating” corrective actionincludes transmitting a signal to perform corrective action, such as amessage, alert, activation signal for an output reduction element 2140,or the like. For instance, and without limitation, processor 2104 may beconfigured to transmit corrective action to apparatus, an outputreduction element 2140, a computing device and/or remote device, and/orto a user.

Still referring to FIG. 21 , processor 2104 and/or detection components2108 may be deployed in subject area according to any suitableconfiguration, including incorporation in one or more apparatuses 100,incorporation in a separate housing, which may be mounted some distancefrom an apparatus 100 to be analyzed or the like. Alternatively oradditionally, processor 2104 and/or one or more detection components2108 may be mounted to and/or on an individual, who may enter subjectarea and interact with one or more apparatuses 100, receiving deterrentoutputs. In an embodiment, intensity, energy, impact levels or otherparameters 2116 to be compared to thresholds may be detected using suchindividual-mounted components, permitting comparison of internallymeasured parameters 2116 to parameters 2116 measuring impact at theindividual. This may be used for calibration or the like.

Referring now to FIG. 22 , an exemplary embodiment of a method 2200 ofoperating an autonomous safety system for a deterrent 2112 apparatus, isillustrated. At step 2205, a processor 2104 detects at least a deterrentparameter 2116 using at least a detection component 2108 communicativelyconnected to at least a deterrent 2112 of a deterrent 2112 apparatus;this may be implemented without limitation as described above inreference to FIGS. 1-11 .

At step 2210, and continuing to refer to FIG. 22 , processor 2104compares at least a deterrent parameter 2116 to a safety threshold 2124;this may be implemented without limitation as described above inreference to FIGS. 1-11 . For instance, and without limitation,parameter 2116 may include irradiance generated by a directed lightdeterrent 2112. As a further non-limiting example, parameter 2116 mayinclude a measure of acoustic intensity. In an additional non-limitingexample, parameter 2116 may include an electrical parameter 2116, suchas without limitation a voltage level, a current, or the like. Parameter2116 may, as a further non-limiting example, include a temperature.

Still referring to FIG. 22 , at least one of the first parameter 2116and the second parameter 2116 may include a cumulative energy value, asdefined above in reference to FIGS. 1-11 . Cumulative energy value mayinclude a per-engagement value.

At step 2215, processor 2104 determines a corrective action as afunction of the comparison; this may be implemented without limitationas described above in reference to FIGS. 1-11 .

At step 2220, processor 2104 initiates the corrective action; this maybe implemented without limitation as described above in reference toFIGS. 1-11 . For instance, and without limitation, processor 2104 maytransmit a corrective action to apparatus. Processor 2104 may transmitcorrective action to an internal watchdog system within apparatus.Processor 2104 may provide corrective action and/or other data to one ormore remote devices.

Referring now to FIG. 23 , an exemplary embodiment of a system 2300 forinitiating a deterrent is illustrated. System 2300 includes an automatedthreat detection and deterrence apparatus 2304. Apparatus 2304 mayinclude any computing device as described in this disclosure, includingwithout limitation a microcontroller, microprocessor, digital signalprocessor (DSP) and/or system on a chip (SoC) as described in thisdisclosure. Apparatus may include, be included in, and/or communicatewith a mobile device such as a mobile telephone or smartphone. Apparatus2304 may include a single computing device operating independently, ormay include two or more apparatus operating in concert, in parallel,sequentially or the like; two or more computing devices may be includedtogether in a single apparatus or in two or more apparatuses. Apparatus2304 may interface or communicate with one or more additional devices asdescribed below in further detail via a network interface device.Network interface device may be utilized for connecting apparatus 2304to one or more of a variety of networks, and one or more devices.Examples of a network interface device include, but are not limited to,a network interface card (e.g., a mobile network interface card, a LANcard), a modem, and any combination thereof. Examples of a networkinclude, but are not limited to, a wide area network (e.g., theInternet, an enterprise network), a local area network (e.g., a networkassociated with an office, a building, a campus or other relativelysmall geographic space), a telephone network, a data network associatedwith a telephone/voice provider (e.g., a mobile communications providerdata and/or voice network), a direct connection between two apparatuses,and any combinations thereof. A network may employ a wired and/or awireless mode of communication. In general, any network topology may beused. Information (e.g., data, software etc.) may be communicated toand/or from a computer and/or computing device. Apparatus 2304 mayinclude but is not limited to, for example, a computing device orcluster of computing devices in a first location and a second apparatusor cluster of apparatuses in a second location. Apparatus 2304 mayinclude one or more apparatuses dedicated to data storage, security,distribution of traffic for load balancing, and the like. Apparatus 2304may distribute one or more computing tasks as described below across aplurality of apparatuses of apparatus, which may operate in parallel, inseries, redundantly, or in any other manner used for distribution oftasks or memory between apparatuses. Apparatus 2304 may be implementedusing a “shared nothing” architecture in which data is cached at theworker, in an embodiment, this may enable scalability of system 2300and/or apparatus. Apparatus 2304 may be configured to communicate withdeterrent components remotely, such that a computing device may receivesensor data and transmit commands to deterrent components, wherein adeterrent is an output of an external stimulus, and a deterrentcomponent generates the output of the external stimulus. Apparatus 2304may include one or more computing devices in addition to one or moredeterrents and/or sensors.

Further referring to FIG. 23 , apparatus 2304 may be designed and/orconfigured to perform any method, method step, or sequence of methodsteps in any embodiment described in this disclosure, in any order andwith any degree of repetition. For instance, apparatus 2304 may beconfigured to perform a single step or sequence repeatedly until adesired or commanded outcome is achieved; repetition of a step or asequence of steps may be performed iteratively and/or recursively usingoutputs of previous repetitions as inputs to subsequent repetitions,aggregating inputs and/or outputs of repetitions to produce an aggregateresult, reduction or decrement of one or more variables such as globalvariables, and/or division of a larger processing task into a set ofiteratively addressed smaller processing tasks. Apparatus 2304 mayperform any step or sequence of steps as described in this disclosure inparallel, such as simultaneously and/or substantially simultaneouslyperforming a step two or more times using two or more parallel threads,processor cores, or the like; division of tasks between parallel threadsand/or processes may be performed according to any protocol suitable fordivision of tasks between iterations. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of various waysin which steps, sequences of steps, processing tasks, and/or data may besubdivided, shared, or otherwise dealt with using iteration, recursion,and/or parallel processing.

Still referring to FIG. 23 , apparatus 2304 is configured to identify abehavior 2308 of an individual. As used in this disclosure a “behavior”is an action and mannerism made by an individual, organism, system, orartificial entities in conjunction with themselves or their environment,which includes the other systems or organisms around as well as thephysical environment. As used in this disclosure an “individual” is aperson. Behavior 2308 may include, without limitation, overt behavior,wherein overt behavior is a visible type of behavior that can occuroutside of a human being. Overt behavior may include, withoutlimitation, eating food, riding a bicycle, playing football, walking ina secure area, or the like thereof. Behavior 2308 may include, withoutlimitation, covert behavior, wherein covert behavior is not visible toanother individual. Covert behavior may include, without limitation,thoughts, emotions, feelings, or the like thereof. Behavior 2308 mayinclude, without limitation, molecular behavior, wherein molecularbehavior includes unexpected behavior that occurs without thinking,which can be broken down into atomistic parts or molecules. Molecularbehavior may include, without limitation, an individual that closestheir eyes when something is about to interact with that individual'seyes. Behavior 2308 may include, without limitation, molar behavior,wherein molar behavior is a behavior that is identified in terms of theultimate cause of history. Molar Behavior may include, withoutlimitation, a person that loves someone is merely exhibiting a patternof loving behavior over time, as love would be considered atomistic andmust be looked in more wholistic terms. Behavior 2308 may include,without limitation, voluntary behavior, wherein voluntary behavior is atype of behavior that depends on a human want, desire, wish, yearning,or the like thereof. Voluntary behavior may include, without limitation,walking, speaking, writing, striking, and the like thereof. Behavior2308 may include, without limitation, involuntary behavior, whereininvoluntary behavior is a behavior that naturally occurs withoutthinking. Voluntary behavior may include, without limitation, breathing,blinking, swallowing, digestion, or the like thereof. Behavior 2308 mayinclude behavior that is considered to be positive, negative, and/orneutral behavior. As used in this disclosure “positive behavior” isbehavior that is perceived by another individual, organism, artificialintelligence, or entity as a good act. As a non-limiting examplepositive behavior may include altruistic behavior, caring behavior,compassionate behavior, considerate behavior, faithful behavior,impartial behavior, kind behavior, pleasant behavior, polite behavior,sincere behavior, and the like thereof. As used in this closure a“negative behavior” is behavior that is perceived by another individual,organism, artificial intelligence, or entity as a bad act; negativebehavior may include behavior associated with a threat level warrantinga deterrent response for instance as described in U.S. Provisional App.Ser. No. 63/067,142. As a non-limiting example, a negative behavior mayinclude aggressive behavior, argumentative behavior, bossy behavior,deceitful behavior, domineering behavior, flaky behavior, inconsideratebehavior, manipulative behavior, rude behavior, spiteful behavior, andthe like thereof. As used in this disclosure “neutral behavior” isbehavior that is perceived by another individual, organism, artificialintelligence, or entity as a behavior that does not attempt to displayany positive or negative intentions. As a non-limiting example, aneutral behavior may include apathetic behavior, indifferent behavior,behavior indicative of a lack of conviction, or the like.

Still referring to FIG. 23 , behavior 2308 may be identified as afunction of a recognition element 2312. As used in this disclosure a“recognition element” is datum obtained from one or more sensors thatprovide information relating to a behavior. As a non-limiting example, arecognition element may be obtained from one or more sensors relating toa voluntary behavior of an individual entering a secure room, which mayindicate a negative behavior of trespassing. As a further non-limitingexample a recognition element may be obtained from one or more sensorsrelating to an involuntary behavior of swallowing, which may indicate anegative behavior of anxiety and/or nervousness. As used in thisdisclosure “sensor” is a device that detects or measures a physicalproperty and records, indicates, or otherwise responds to the detectedor measured physical property. Sensors may be comprised of one or moreof imaging and other sensors, such as optical cameras, infrared cameras,3D cameras, multispectral cameras, hyperspectral cameras, polarizedcameras, chemical sensors, motion sensors, ranging sensors, light radarcomponent, such as lidar, detection or imaging using radio frequenciescomponent, such as radar, terahertz or millimeter wave imagers, seismicsensors, magnetic sensors, weight/mass sensors, ionizing radiationsensors, and/or acoustical sensors. Sensors may alternatively oradditionally include any device used as a sensor as described in U.S.Provisional App. Ser. No. 63/067,142.

Still referring to FIG. 23 , identifying behavior 2308 may be identifiedas a function of a behavior machine learning process 2316. As used inthis disclosure “behavior machine-learning process” is amachine-learning process that uses training data and/or training set togenerate an algorithm that will be performed by an apparatus and/or oneor more remote devices to produce outputs given data provided as inputs;this is in contrast to a non-machine learning software program where thecommands to be executed are determined in advance by a user and writtenin a programming language. Behavior machine-learning process 2316 mayinclude any supervised, unsupervised, or reinforcement machine-learningprocess that apparatus 2304 may or may not use in the determination ofthe behavior. Behavior machine-learning process 2316 may include,without limitation machine learning processes such as simple linearregression, multiple linear regression, polynomial regression, supportvector regression, ridge regression, lasso regression, elasticnetregression, decision tree regression, random forest regression, logisticregression, logistic classification, K-nearest neighbors, support vectormachines, kernel support vector machines, naive bayes, decision treeclassification, random forest classification, K-means clustering,hierarchical clustering, dimensionality reduction, principal componentanalysis, linear discriminant analysis, kernel principal componentanalysis, Q-learning, State Action Reward State Action (SARSA), Deep-Qnetwork, Markov decision processes, Deep Deterministic Policy Gradient(DDPG), or the like thereof. Behavior machine-learning process 2316 maybe trained as a function of a behavior training set 2320. As used inthis disclosure “behavior training set” is a training set thatcorrelates at least a user action to at least a behavioral datum. Asused in this disclosure a “user action” is a physical, psychological,and/or spiritual decision that is acted upon, wherein that action atleast impacts one or more surrounding individuals. For example, andwithout limitation a user action may include striking an individual,walking into a secure area, coughing on an individual, assaulting anindividual, verbally abusing an individual, verbally discriminatingagainst another individual's religious beliefs, sitting on a chair,complimenting an individual, opening a door for an individual and thelike thereof. As used in this disclosure a “behavioral datum” isinformation that at least relates to a user's intended behaviordecision. Behavioral data may include, without limitation,microexpression, macroexpressions, language, tone, word selection,physiological actions, and the like thereof. As a non-limiting example,behavioral data may relate a microexpression of a nose wrinkled with anegative behavior of disgust.

Still referring to FIG. 23 , behavior machine-learning process 2316 maybe generated as a function of a classifier. A “classifier,” as used inthis disclosure is a machine-learning model, such as a mathematicalmodel, neural net, or program generated by a machine learning algorithmknown as a “classification algorithm,” as described in further detailbelow, that sorts inputs into categories or bins of data, outputting thecategories or bins of data and/or labels associated therewith. Aclassifier may be configured to output at least a datum that labels orotherwise identifies a set of data that are clustered together, found tobe close under a distance metric as described below, or the like.Apparatus 2304 and/or another device may generate a classifier using aclassification algorithm, defined as a process whereby apparatus 2304derives a classifier from training data. Classification may be performedusing, without limitation, linear classifiers such as without limitationlogistic regression and/or naive Bayes classifiers, nearest neighborclassifiers such as k-nearest neighbors classifiers, support vectormachines, least squares support vector machines, fisher's lineardiscriminant, quadratic classifiers, decision trees, boosted trees,random forest classifiers, learning vector quantization, and/or neuralnetwork-based classifiers.

Still referring to FIG. 23 , apparatus 2304 may be configured togenerate a classifier using a Naïve Bayes classification algorithm.Naïve Bayes classification algorithm generates classifiers by assigningclass labels to problem instances, represented as vectors of elementvalues. Class labels are drawn from a finite set. Naïve Bayesclassification algorithm may include generating a family of algorithmsthat assume that the value of a particular element is independent of thevalue of any other element, given a class variable. Naïve Bayesclassification algorithm may be based on Bayes Theorem expressed asP(A/B)=P(B/A) P(A)÷P(B), where P(A/B) is the probability of hypothesis Agiven data B also known as posterior probability; P(B/A) is theprobability of data B given that the hypothesis A was true; P(A) is theprobability of hypothesis A being true regardless of data also known asprior probability of A; and P(B) is the probability of the dataregardless of the hypothesis. A naive Bayes algorithm may be generatedby first transforming training data into a frequency table. Apparatus2304 may then calculate a likelihood table by calculating probabilitiesof different data entries and classification labels. Apparatus 2304 mayutilize a naive Bayes equation to calculate a posterior probability foreach class. A class containing the highest posterior probability is theoutcome of prediction. Naïve Bayes classification algorithm may includea gaussian model that follows a normal distribution. Naïve Bayesclassification algorithm may include a multinomial model that is usedfor discrete counts. Naïve Bayes classification algorithm may include aBernoulli model that may be utilized when vectors are binary.

With continued reference to FIG. 23 , apparatus 2304 may be configuredto generate a classifier using a K-nearest neighbors (KNN) algorithm. A“K-nearest neighbors algorithm” as used in this disclosure, includes aclassification method that utilizes feature similarity to analyze howclosely out-of-sample-features resemble training data to classify inputdata to one or more clusters and/or categories of features asrepresented in training data; this may be performed by representing bothtraining data and input data in vector forms, and using one or moremeasures of vector similarity to identify classifications withintraining data, and to determine a classification of input data.K-nearest neighbors algorithm may include specifying a K-value, or anumber directing the classifier to select the k most similar entriestraining data to a given sample, determining the most common classifierof the entries in the impact, and classifying the known sample; this maybe performed recursively and/or iteratively to generate a classifierthat may be used to classify input data as further samples. Forinstance, an initial set of samples may be performed to cover an initialheuristic and/or “first guess” at an output and/or relationship, whichmay be seeded, without limitation, using expert input received accordingto any process as described herein. As a non-limiting example, aninitial heuristic may include a ranking of associations between inputsand elements of training data. Heuristic may include selecting somenumber of highest-ranking associations and/or training data elements.

With continued reference to FIG. 23 , generating k-nearest neighborsalgorithm may generate a first vector output containing a data entrycluster, generating a second vector output containing an input data, andcalculate the distance between the first vector output and the secondvector output using any suitable norm such as cosine similarity,Euclidean distance measurement, or the like. Each vector output may berepresented, without limitation, as an n-tuple of values, where n is atleast two values. Each value of n-tuple of values may represent ameasurement or other quantitative value associated with a given categoryof data, or attribute, examples of which are provided in further detailbelow; a vector may be represented, without limitation, in n-dimensionalspace using an axis per category of value represented in n-tuple ofvalues, such that a vector has a geometric direction characterizing therelative quantities of attributes in the n-tuple as compared to eachother. Two vectors may be considered equivalent where their directions,and/or the relative quantities of values within each vector as comparedto each other, are the same; thus, as a non-limiting example, a vectorrepresented as [5, 10, 15] may be treated as equivalent, for purposes ofthis disclosure, as a vector represented as [1, 2, 3]. Vectors may bemore similar where their directions are more similar, and more differentwhere their directions are more divergent; however, vector similaritymay alternatively or additionally be determined using averages ofsimilarities between like attributes, or any other measure of similaritysuitable for any n-tuple of values, or aggregation of numericalsimilarity measures for the purposes of loss functions as described infurther detail below. Any vectors as described herein may be scaled,such that each vector represents each attribute along an equivalentscale of values. Each vector may be “normalized,” or divided by a“length” attribute, such as a length attribute 1 as derived using aPythagorean norm: l=√{square root over (Σ_(i=0) ^(n)a_(i) ²)}, where a,is attribute number i of the vector. Scaling and/or normalization mayfunction to make vector comparison independent of absolute quantities ofattributes, while preserving any dependency on similarity of attributes;this may, for instance, be advantageous where cases represented intraining data are represented by different quantities of samples, whichmay result in proportionally equivalent vectors with divergent values.

Further referring to FIG. 23 , behavior classifier may include anybehavior classifier as described in as described in U.S. ProvisionalApp. Ser. No. 63/067,142.

Still referring to FIG. 23 , identifying a behavior may includereceiving a psychological indicator 2324. As used in this disclosure a“psychological indicator” is an indicator of cognitive decisions and/oractions related to a user's intended behavior. Psychological indicatorsmay include, without limitation cognitive abilities, emotionalabilities, social and networking abilities, and extraneous aptitudes. Asused in this disclosure “cognitive abilities” are psychologicalcharacteristics a user may perform that relates to at least a userbehavior. For example, and without limitation, clarity of thought,concentration capacity, lucidity, attention, linguistic ability,decision-making, memory, visual-spatial ability, cognitive flexibility,mental agility, mathematical ability, and the like thereof. As used inthis disclosure “emotional abilities” are emotional elements that relateto at least a user behavior. For example, and without limitationemotional abilities may include empathy, emotional stabilities,relaxation, adaptation to stress, emotional comfort, impulsivity,impulse control, emotionality, and the like thereof. As used in thisdisclosure “social and networking abilities” include psychologicalelements that relate to a user behavior in relation to otherindividuals. For example, and without limitation, social and networkingabilities may include authority, assertiveness, sense of belonging to agroup, respect for others, leadership, sociability, tolerance toopposing views, conformity, interpersonal trust, and the like thereof.As used in this disclosure “extraneous aptitudes” are psychologicalelements that relate a user's psychological history and/or tendencies toa user behavior. For example, and without limitation extraneousaptitudes may include vitality, self-preservation, vigilance, ambition,dynamism, courage, selflessness, righteous attitude, responsibility,perseverance, patience, realism, creativity, force of character,generosity, oratorical ability, curiosity, diligence, trustworthiness,honesty, thrift, adaptability, objectivity, self-assertion, willpower,ego indicator, self-control, intuition, inventiveness, autonomy,optimism, self-confidence, mental calmness, and the like thereof.

Still referring to FIG. 23 , apparatus 2304 is configured to determine acandidate deterrent space 2328 as a function of behavior 2308. As usedin this disclosure “candidate deterrent space” is a set of all possiblecombinations of deterrents, at all possible intensity, energy level,frequency, or other measures of degree, such that any such combinationthat may be selected by selecting a possible value of a deterrentvariable, or of a plurality of deterrent variables, is represented inthe candidate deterrent space, where “possible values” may include allphysically or practically feasible values, all values as dictated by oneor more constraints, or the like. As used in this disclosure a“deterrent” is a thing, entity, object, and/or action that intends todiscourage and/or prevent an individual from continuing an action,behavior, and/or conduct. Deterrent 2344 may include without limitation,directed light, sounds, electrical deterrents, neurostimulators,chemicals, entanglement devices, and the like thereof. As used in thisdisclosure a “directed light deterrent” is a deterrent that uses ahigh-intensity light source such as, but not limited to, a laser, superLED, laser illuminated LED, super-luminescent LED, VCSEL, plasmadischarge lamp, and/or high-intensity LED that is actively aimed atand/or focused on an individual, to generate a deterrent effect. As usedin this disclosure a “directed sound deterrent” is a sound source thatis aimed at a specific individual in a manner analogous to a directedlight source. A directed sound deterrent may include, without limitationa long-range acoustic device (LRAD), a laser generating localizedplasmas in the atmosphere to create modulating plasmas near theindividual such that audible sound is produced, an ultrasonic carrierwave, and the like thereof. As used in this disclosure “neurostimulationdeterrents” is an electrical source that is projected at an individualsuch that an electrical contact is achieved between an individual andthe deterrent. As a non-limiting example, an electrical shock deterrentmay include a Human Electro-Muscular Incapacitation (HEMI) device, astun gun, a taser, Taser Area Denial System (TADS), a plasma, anelectric field, an ionizer, and the like thereof. As used in thisdisclosure a “chemical deterrent” is a chemical and/or molecule that atleast provide a noxious and/or discomforting experience for anindividual. For example, and without limitation, a chemical deterrentmay include pepper spray, malodorant weapons, tear gas, pacifying agent,white phosphorous, aerosolized opioids, and the like thereof. As used inthis disclosure “entanglement devices” are deterrents wherein anindividual becomes physically trapped in a device and prevents escape ofthat individual. For example, an entanglement device may include,without limitation nets, bolas, and/or other entanglement or entrapmentdevices that are launched ballistically at the individual in order tolimit or stop the individual's ability to move normally. As used in thisdisclosure a “deterrent variable” is a parameter according to whichcommands to a deterrent component and/or parameters of a deterrentproduced thereby can be altered and/or modified. As used in thisdisclosure a “deterrent component” is the source that generates adeterrent output. As a non-limiting example a deterrent component adirected light deterrent component such as a laser or the like, whichmay output directed light such as a laser beam or other directionallight output, in a pattern or at an intensity level that produces anaversive effect when shone in the eyes of an individual. As anon-limiting example, a deterrent variable may include energy output,energy intensity, energy duty cycle, energy on/off cycle, energylimiter, audio quality, audio intensity, audio duration, audio pulse,audio location, light intensity, light duty cycle, light pulse, lightlocation, light color, laser intensity, laser duty cycle, laser pulse,laser wavelength, current, voltage, wattage, and the like thereof.Deterrent space may incorporate one or more deterrent variables inrelation to one or more deterrents and/or deterrent components. Forexample and without limitation a super-luminescent LED may includedeterrent variables of intensity, wavelength, duty cycle, power,current, voltage, emission rate, and/or wattage, which may all bealtered and/or controlled in the same candidate deterrent space. As afurther non-limiting example, deterrent variables of phenacyl chloridemay include concentration, distance exposed, duration of exposure,location of exposure, diffusivity, diffusion, evaporation, chemicalcross-reactivity, and the like thereof. Additionally or alternatively,an audible output may have deterrent variables associated with audiblequality, decibel intensity, wavelength, amplitude, time-period,frequency, velocity of wave, audio duty cycle, audio location, and thelike thereof.

Still referring to FIG. 23 , apparatus 2304 is configured to determineat least a deterrent impact 2336. As used in this disclosure a“deterrent impact” is an effect an external stimulus has on a user. Forexample, and without limitation, deterrent impact 2336 may includecovering of ears to attempt to block out an audio signal as a result ofan external stimulus of a 10,000 Hz audio signal. Deterrent impact 2336may be comprised of a measurable element associated with an effect thedeterrent has on a user. Deterrent Impact 2336 may provide an overallvalue associated with the likelihood that an individual may or may notbe harmed or otherwise negatively impacted. As a non-limiting example adeterrent impact may be comprised of a value of 10 indicating a strongpropensity for causing dermal burns on an individual. For example, andwithout limitation, a deterrent impact may be identified as a value of40 for a laser pulse, indicating a strong possibility for temporaryblindness, and a value of 20 for an auditory output, indicating a weakpossibility for auditory discomfort for a behavior response of exitingthe room. Additionally or alternatively, a deterrent impact may identifyone or more impacts associated with an external stimulus. As anon-limiting example a deterrent impact may include temporary blindness,damage to vision, temporary incapacitation, nausea, seizures,post-traumatic stress disorder and/or retinal burning associated with anexternal stimulus of a laser or other directed light deterrent. As afurther non-limiting example a deterrent impact may include temporarypermanent hearing loss, tinnitus, sensorineural hearing loss, conductivehearing loss, post-traumatic stress disorder, auditory nerve damage,pain or other deleterious sensory experiences in the ears, and/orruptured eardrums associated with an external stimulus of an audiosignal. As a further non-limiting example, a deterrent impact mayinclude loss of consciousness, muscle spasms, numbness, tingling,breathing disorders, headache, electrical burns, heart arrythmias,and/or tetanus associated with an external stimulus of an electricalshock. As a further non-limiting example, a deterrent impact may includeincapacitation, nerve damage, muscle contraction, nausea, headache,stomachache, and/or seizures associated with an external stimulus of aneurostimulant. As a further non-limiting example, a deterrent impactmay include incapacitation, cross-reactivity, burning sensation,permanent nerve damage, nausea, vomiting, and/or blindness associatedwith an external stimulus of a chemical. As a further non-limitingexample, a deterrent impact may include muscle contraction, blunt forcetrauma, loss of limb, and/or puncture wound associated with an externalstimulus of an entanglement device. A deterrent impact may include anydegree of severity of any of the above-described deterrent impacts. As anon-limiting example blunt force trauma may exist within a degree ofseverity including, but not limited to, contusions, abrasions,lacerations, internal hemorrhages, and/or bone fractures.

Still referring to FIG. 23 , apparatus 2304 is configured to determineat least a behavior response 2340. As used in this disclosure a“behavior response” is an action and/or response that an individual maycomplete as an effect of the deterrent impact. Behavior response 2340may include, without limitation, exiting a room, ceasing verbalcommunication, moving away from the external stimulus, covering one'sears or eyes, removing their body from the contact of the externalstimulus, or the like thereof. As a non-limiting example a behaviorresponse may include an individual that exits a secure area as a resultof an audio output. As a further non-limiting example a behaviorresponse of muscular contraction may occur as a result of an electricalshock. As a further non-limiting example, a behavior response ofsneezing may occur as a result of a chemical deterrent. Behaviorresponse data may be identified as a function of one or more sensors,wherein a sensor is described in detail above. For example, and withoutlimitation, a sensor may indicate an individual seized after receivingan electrical shock deterrent at an output of 40 mW. Behavior responsemay be determined as a function of one or more behavior storagememories. As used in this disclosure a “behavior storage memory” is amemory device that may or may not exist within apparatus 2304 thatstores previously identified behavior responses. Behavior response mayrelate a previous behavior response of muscular contraction due to anelectrical response and identify a current behavior response of tinnitusdue to an electrical response.

Still referring to FIG. 23 , apparatus 2304 may receive a target levelfunction 2332 of candidate deterrent space 2328. As used in thisdisclosure a “target level function” is an algorithm associated with thecandidate deterrent space that identifies the at least deterrentvariables possible modifications thereto and outputs a deterrent impactas a function of such variables and possible modifications. Target levelfunction 2332 may use a linear function that inputs the candidatedeterrent space including the at least deterrent variables possible andoutputs a deterrent impact using the linear function. Target levelfunction 2332 may utilize a neural net that receives the plurality ofdeterrent variables from the candidate deterrent space and generates thedeterrent impact as a function of the neural net algorithm. Target levelfunction 2332 may be received as a function of one or more remotedevices transmitting the target level function to apparatus 2304. Targetlevel function 2332 may be generated as a function of an impact machinelearning process 2336. As used in this disclosure “impact machinelearning process” is a machine-learning process that uses training dataand/or training set to generate an algorithm that will be performed byan apparatus and/or one or more remote devices to produce outputs givendata provided as inputs; this is in contrast to a non-machine learningsoftware program where the commands to be executed are determined inadvance by a user and written in a programming language. The impactmachine-learning process may include any supervised, unsupervised, orreinforcement machine-learning process that apparatus 2304 may or maynot use in the determination of the user condition. The impactmachine-learning process may include, without limitation machinelearning processes such as simple linear regression, multiple linearregression, polynomial regression, support vector regression, ridgeregression, lasso regression, elasticnet regression, decision treeregression, random forest regression, logistic regression, logisticclassification, K-nearest neighbors, support vector machines, kernelsupport vector machines, naive bayes, decision tree classification,random forest classification, K-means clustering, hierarchicalclustering, dimensionality reduction, principal component analysis,linear discriminant analysis, kernel principal component analysis,Q-learning, State Action Reward State Action (SARSA), Deep-Q network,Markov decision processes, Deep Deterministic Policy Gradient (DDPG), orthe like thereof. The impact machine-learning process is generated as afunction of an impact training set. The impact training set relates adeterrent impact to at least a behavior response, wherein a deterrentimpact and behavior response is described in detail above. Target impactlevel function 2332 may be generated to at least identify a valueassociated with a deterrent impact in relation to a behavior response.For example, and without limitation, a deterrent impact may beidentified as a value of 40 for a laser pulse, indicating a strongpossibility for temporary blindness, and a value of 20 for an auditoryoutput, indicating a weak possibility for auditory discomfort for abehavior response of exiting the room.

Still referring to FIG. 23 , apparatus 2304 may receive target levelimpact function 2332 from one or more remote devices. As used in thisdisclosure a “remote device” is a computing system external to theapparatus that obtains and/or sends information relating to the targetlevel impact function. The remote devices may provide target levelimpact function using one or more impact machine-learning processes,wherein an impact machine-learning process is described above in detail.The remote device may perform the impact machine-learning process, usingan impact training set, wherein an impact training set is describedabove in detail. The remote device may transmit a signal, bit, datum, orparameter to apparatus 2304 that at least relates to target level impactfunction 2332. Additionally or alternatively, the remote devices mayprovide modifications to the generated target level impact functions.For example, and without limitation, a modification may be comprised ofa firmware update, a software update, an impact machine-learning modelcorrection, and the like thereof. As a non-limiting example a softwareupdate may incorporate a new target level impact function that relatesto a modified candidate deterrent space that alters or otherwise changesthe deterrent variables. As a further non-limiting example a remotedevice may transmit a modified impact machine-learning process, whereinthe modified impact machine-learning model may relate new behaviorresponses to previously identified deterrent impacts. Additionally oralternatively, target level impact function 2332 may be transmitted tothe remote device, wherein the remote device may update the impacttraining data and transmit an updated impact machine-learning processback to apparatus 2304. The updated impact machine-learning process maybe transmitted by the remote device and received by apparatus 2304 as asoftware update, firmware update, or corrected impact machine-learningprocess.

Still referring to FIG. 23 , apparatus 2304 is configured to select adeterrent 2344 from candidate deterrent space 2328 that minimizes targetimpact level function 2332. Target level function 2332 may be minimizedby determining a behavior modification of the candidate deterrent spacerelated to a behavior modification function. As used in this disclosurea “behavior modification” is a modified action and mannerism made by anindividual, organism, system, or artificial entities in conjunction withthemselves or their environment, which includes the other systems ororganisms around as well as the physical environment, such that actionhas been modified to eliminate the first action. For example, andwithout limitation a behavior modification may include a first behaviorof angry and/or violent to a modified behavior to content and/orsatisfied. As used in this disclosure a “behavior modification function”is the intended behavior modification that a deterrent is meant toperform. For example, and without limitation a behavior modificationfunction may identify an intended deterrent function of eliminating averbally abusive behavior of an individual. As a further non-limitingexample a behavior modification function may identify an intendeddeterrent function of an individual halting and retreating. As a furthernon-limiting example a behavior modification function may identify anintended deterrent function of cessation of violent and/or damagingactions. As a further non-limiting example a behavior modificationfunction may identify an intended deterrent function of altering abehavior from a negative behavior to a positive behavior. As a furthernon-limiting example a behavior modification function may identify anintended deterrent function of incapacitation. As a further non-limitingexample a behavior modification function may identify an intendeddeterrent function of entanglement. Minimizing the target level impactfunction may include minimizing the deterrent impact, while maximizingthe behavior modification function such that the individual receives theleast invasive deterrent that generates a maximum behavior modification.Target level function 2332 may be minimized using an optimization and/orlinear optimization function to at least reduce the deterrent impact andenhance the behavior modification. A linear objective function may beused to minimized target level function 2332, wherein apparatus 2304 mayuse a linear program such as, without limitation, a mixed-integerprogram. A “linear program,” as used in this disclosure, is a programthat optimizes a linear objective function, given at least a constraint.As a non-limiting example, apparatus 2304 may calculate variables of setof deterrent impacts of such parameters from goal parameters calculatean output of behavioral modifications using the variables, and select adeterrent with a deterrent output having the lowest size, according to agiven definition of “size,” of the set of deterrent outputs representingthe selected deterrents; size may, for instance, include absolute value,numerical size, or the like.

With continued reference to FIG. 23 , optimizing target level function2332 may include minimizing a loss function, where a “loss function” isan expression an output of which an optimization algorithm minimizes togenerate an optimal result. As a non-limiting example, apparatus 2304may assign variables relating to a set of parameters, which maycorrespond to deterrent impacts as described above, calculate an outputof mathematical expression using the variables, and select a deterrentthat produces an output having the lowest size, according to a givendefinition of “size,” of the set of outputs representing each of theselected deterrents; size may, for instance, included absolute value,numerical size, or the like. Selection of different loss functions mayresult in selection of different deterrents as generating minimaloutputs.

Still referring to FIG. 23 , the behavior modification function may begenerated as a function of a modification machine-learning process. Asused in this disclosure a “modification machine-learning process” is amachine-learning process that uses training data and/or training set togenerate an algorithm that will be performed by an apparatus and/or oneor more remote devices to produce outputs given data provided as inputs;this is in contrast to a non-machine learning software program where thecommands to be executed are determined in advance by a user and writtenin a programming language. The behavior machine-learning process mayinclude any supervised, unsupervised, or reinforcement machine-learningprocess that apparatus 2304 may or may not use in the determination ofthe user condition. The behavior machine-learning process may include,without limitation machine learning processes such as simple linearregression, multiple linear regression, polynomial regression, supportvector regression, ridge regression, lasso regression, elasticnetregression, decision tree regression, random forest regression, logisticregression, logistic classification, K-nearest neighbors, support vectormachines, kernel support vector machines, naive bayes, decision treeclassification, random forest classification, K-means clustering,hierarchical clustering, dimensionality reduction, principal componentanalysis, linear discriminant analysis, kernel principal componentanalysis, Q-learning, State Action Reward State Action (SARSA), Deep-Qnetwork, Markov decision processes, Deep Deterministic Policy Gradient(DDPG), or the like thereof. The behavior machine-learning process maybe generated as a function of a behavior training set. As used in thisdisclosure a “behavior training set” relates at least a behaviormodifier with at least an expected vector. As used in this disclosure a“behavior modifier” is an element in which a behavior may be altered orotherwise changed according to at least an environment factor,categorical factor, and/or individual history. For example, and withoutlimitation, a behavior modifier may include categorical information ofan individual that has a predisposition to anxiety when a certain audioinput is received by the individual. As used in this disclosure an“expected vector” is a predicted outcome according to the behaviormodifier. For example, and without limitation, an expected outcome mayinclude loss of attention to a previous situation when a behaviormodifier associated with attention deficit disorder.

Still referring to FIG. 23 , target level impact function 2332 may beminimized with respect to at least a deterrent output constraint. Asused in this disclosure a “deterrent output constraint” is a limit thatis established for a deterrent output such that the deterrent outputcannot operate outside of the limit. A limit may include an upperconstraint, wherein the upper constraint is a maximum value establishedfor a deterrent variable in candidate deterrent space 2328. For example,and without limitation a deterrent output constraint may include a limitwith an upper constraint of 100 mW for a 732 nm wavelength laser. As afurther non-limiting example a deterrent output constraint may include alimit with an upper constraint of 20 m/z ballistic velocity for anentanglement device. A limit may include a lower constraint, wherein thelower constraint is a minimum value established for a deterrent variablein candidate deterrent space 2328. For example, and without limitation,a deterrent output constraint may include a limit with a lowerconstraint of 5 ppb phenacyl chloride for a chemical release deterrent.As a further non-limiting example, a deterrent output constraint mayinclude a limit with a lower constraint of 5 mA of current for aneurostimulation deterrent.

Still referring to FIG. 23 , apparatus 2304 may be configured toinitiate deterrent 2344 and receive at least a feedback input using atleast a sensor. As used in this disclosure a “feedback input” isinformation received that at least relates to a behavioral response ofan individual. As used in this disclosure a “behavioral response” is areaction by an individual such that an altered behavior is at leastdetected using a sensor, wherein a sensor is defined in detail above.For example, and without limitation a feedback input may includedetecting a behavioral response of incapacitation as a result of a firstdeterrent of a chemical release. The feedback input may be received byobtaining a first behavioral response associated with a first deterrentand determining a corrected impact level function as a function of thefirst behavioral response. As used in this disclosure, a “correctedimpact level function” is an algorithm that at least relates theintended behavioral modification to the first behavioral responsereceived by the feedback input. For example, and without limitation acorrected impact level function may relate an intended behaviormodification of entanglement of an individual to an actual behavioralresponse of blunt force trauma to an individual from a deterrent. Thecorrected level function may be completed by one or more correctionmachine-learning processes. As used in this disclosure a “correctionmachine-learning process” is a machine-learning process that usestraining data and/or training set to generate an algorithm that will beperformed by an apparatus and/or one or more remote device to produceoutputs given data provided as inputs; this is in contrast to anon-machine learning software program where the commands to be executedare determined in advance by a user and written in a programminglanguage. The correction machine-learning process may include anysupervised, unsupervised, or reinforcement machine-learning process thatapparatus 2304 may or may not use in the determination of the usercondition. The correction machine-learning process may include, withoutlimitation machine learning processes such as simple linear regression,multiple linear regression, polynomial regression, support vectorregression, ridge regression, lasso regression, elasticnet regression,decision tree regression, random forest regression, logistic regression,logistic classification, K-nearest neighbors, support vector machines,kernel support vector machines, naive bayes, decision treeclassification, random forest classification, K-means clustering,hierarchical clustering, dimensionality reduction, principal componentanalysis, linear discriminant analysis, kernel principal componentanalysis, Q-learning, State Action Reward State Action (SARSA), Deep-Qnetwork, Markov decision processes, Deep Deterministic Policy Gradient(DDPG), or the like thereof. The correction machine-learning process maybe generated as a function of a correction training set. As used in thisdisclosure a “correction training set” relates at least a behavioralresponse to a deterrent applied, wherein a behavioral response isdiscussed in detail above. As used in this disclosure a “deterrentapplied” is a deterrent that was selected and utilized on an individual,wherein a deterrent applied may include all of the deterrent statedabove. For example and without limitation the correction training setmay include relating a behavioral response of quieting an individual toa deterrent applied of an audio output.

Referring now to FIG. 24 , an exemplary embodiment of a system 2400 forreceiving feedback input 2404 is illustrated. Apparatus 2304 may selecta deterrent output and initiate a first deterrent 2408 to an individual.As used in this disclosure a “first deterrent” is one or more deterrentsthat are identified according to a target impact level function, asdescribed above, in reference to FIG. 23 . First deterrent 2408 mayelicit a first behavioral response 2412. As used in this disclosure a“first behavioral response” is a first reaction by an individual suchthat an altered behavior is at least detected using a sensor, wherein asensor is defined in detail above, in reference to FIG. 23 . Apparatus2304 may obtain first behavioral response 2412 and determine a correctedimpact level function 2416 according to a correction machine-learningprocess 2420, wherein a corrected impact level function is an algorithmthat at least relates the intended behavioral modification to firstbehavioral response 2412. As used in this disclosure a “correctionmachine-learning process” is a machine-learning process as describedabove. For instance, and without limitation, a correctionmachine-learning process may include any supervised, unsupervised, orreinforcement machine-learning process that apparatus 2304 may or maynot use in the determination of the user condition. The correctionmachine-learning process may include, without limitation machinelearning processes such as simple linear regression, multiple linearregression, polynomial regression, support vector regression, ridgeregression, lasso regression, elasticnet regression, decision treeregression, random forest regression, logistic regression, logisticclassification, K-nearest neighbors, support vector machines, kernelsupport vector machines, naive bayes, decision tree classification,random forest classification, K-means clustering, hierarchicalclustering, dimensionality reduction, principal component analysis,linear discriminant analysis, kernel principal component analysis,Q-learning, State Action Reward State Action (SARSA), Deep-Q network,Markov decision processes, Deep Deterministic Policy Gradient (DDPG), orthe like thereof. The correction machine-learning process may begenerated as a function of a correction training set 2424. As used inthis disclosure a “correction training set” relates at least abehavioral response to a deterrent applied, wherein a behavioralresponse is discussed in detail above, in reference to FIG. 23 . As usedin this disclosure a “deterrent applied” is a deterrent that wasutilized on an individual, wherein a deterrent applied may include allof the deterrent stated above. For example and without limitation thecorrection training set may include relating a behavioral response ofquieting an individual to a deterrent applied of an audio output.

Now referring to FIG. 25 , an exemplary embodiment of a method 2500 forselecting a deterrent is illustrated. At step 2505, an apparatus 2304identifies a behavior 2308 of an individual. Behavior 2308 includes anyof the behavior 2308 as described above. Behavior 2308 may include overtbehavior, covert behavior, moral behavior, molecular behavior, voluntarybehavior, involuntary behavior, and the like thereof. Behavior 2308 maybe comprised of positive behavior, negative behavior, and/or neutralbehavior. Behavior 2308 may be identified as a function of a recognitionelement 2312. Recognition element 2312 includes any of the recognitionelement 2312 as described above, in reference to FIGS. 1-3 . Recognitionelement 2312 may include, without limitation, datum obtained from one ormore sensors, such as temperature, movement, intention, distance, andthe like thereof. Behavior 2308 may be identified as a function of abehavior machine-learning process 2316 that is performed by apparatus2304 and/or one or more remote devices. Behavior machine-learningprocess 2316 includes any of the behavior machine-learning process 2316as described above, in reference to FIGS. 1-3 . For instance, andwithout limitation, behavior machine-learning process 2316 may include asupervised machine-learning process or an unsupervised machine-learningprocess. Behavior machine learning process 2316 may include aclassification process, such as for example naive Bayes, k-nearestneighbor, decision tree, and/or random forest. Classification processesinclude any of the classification processes as described above inreference to FIGS. 1-3 . Behavior machine-learning process 2316 may beconfigured using a behavior training set 2320. Behavior training set2320 includes any of the behavior training set 2320 as described abovein reference to FIGS. 1-3 . Behavior training set 2320 may include,without limitation, user actions, such as movements, language,intentions, and the like thereof that correlate to behavioral datum,such as intended behavior decisions. For example, and without limitationa behavior training set may relate a user action assault to a behaviordatum of aggression and/or anger. Behavior 2308 may be identified as afunction of a psychological indicator 2324. Psychological indicator 2324includes all of the psychological indicator 2324 as described above, inreference to FIGS. 1-3 . For instance, and without limitation,psychological indicator 2324 may include cognitive abilities, emotionalabilities, social and networking abilities, and/or extraneous aptitudes.

Still referring to FIG. 25 , at step 2510, apparatus 2304 determines acandidate deterrent space 2328 as a function behavior 2308. Candidatedeterrent space 2328 includes any of the candidate deterrent space 2328as described above in reference to FIGS. 1-3 . For instance and withoutlimitation candidate deterrence space 2328 may include a deterrentvariable of a plurality of deterrent variables, wherein a deterrentvariable relates to one or more elements that control the function ofone or more deterrent outputs. As a non-limiting example, a candidatedeterrent space may encompass deterrent variables of energy output,energy intensity, energy duty cycle, energy on/off cycle, energylimiter, audio quality, audio intensity, audio duration, audio pulse,audio location, light intensity, light duty cycle, light pulse, lightlocation, light color, laser intensity, laser duty cycle, laser pulse,laser wavelength, current, voltage, wattage, and the like thereof.

Still referring to FIG. 25 , at step 2515, apparatus 2304 receives atarget impact level function 2332 of candidate deterrent space 2328.Target impact level function 2332 includes any of the target impactlevel function 2332 as described above, in reference to FIGS. 1-3 .Target impact level function 2332 may include, without limitation, analgorithm associated with the candidate deterrent space that relates abehavior of theft to a candidate deterrent space of laser intensity,energy, and wavelength required. Target level function 2332 is receivedby determining a deterrent impact 2336. Deterrent Impact 2336 includesany of the deterrent impact 2336 as described above, in reference toFIGS. 1-3 . For example, and without limitation, deterrent impact 2336may include covering of ears to attempt to block out an audio signal asa result of an external stimulus of a 10,000 Hz audio signal. Targetimpact level function is generated by determining at least a behaviorresponse 2340. Behavior response 2340 includes any of the behaviorresponse as described above, in reference to FIGS. 1-3 . For example,and without limitation, behavior response 2340 may include, exiting aroom, ceasing verbal communication, moving away from the externalstimulus, covering one's ears or eyes, removing their body from thecontact of the external stimulus, or the like thereof. Target impactlevel function 2332 may be generated to at least identify a valueassociated with deterrent impact 2336 in relation to behavior response2340. Target impact level function 2332 may be received as a function ofan impact machine-learning process 2336. Impact machine-learning process2336 includes any of the impact machine-learning process 2336 asdescribed above, in reference to FIGS. 1-3 . For instance, and withoutlimitation, impact machine-learning process may include a supervisedmachine-learning process or an unsupervised machine-learning process.The impact machine learning process may include a classificationprocess, such as for example naive Bayes, k-nearest neighbor, decisiontree, and/or random forest. Classification processes include any of theclassification processes as described above in reference to FIGS. 1-3 .Impact machine-learning process 2336 may be configured using an impacttraining set 2340. Impact training set 2340 includes any of impacttraining set 2340 as described above in reference to FIGS. 1-3 . Impacttraining set 2340 may include, without limitation, deterrent impactsthat relate to at least a behavior response. For example, and withoutlimitation an impact training set may relate a deterrent impact ofsevere discomfort or pain to a behavior response of escaping, movingaway from the deterrent, and/or fleeing.

Still referring to FIG. 25 , at step 2520, apparatus 2304 selects adeterrent 2344 from candidate deterrent space 2328 that minimizes targetimpact level function 2332. Deterrent 2344 includes any of the deterrent2344 as described above, in reference to FIGS. 1-3 . In an embodiment,deterrent 2344 may include, directed light, sounds, electrical signals,neurostimulators, chemicals, entanglement devices, and the like thereof.For example, and without limitation, a deterrent consisting of a tasermay be utilized as both an electrical signal and neurostimulator.

Still referring to FIG. 25 , at step 2525, apparatus 2304 initiatesdeterrent 2344. Initiation of deterrent 2344 may include performance ofa first step in the application of a deterrent; first step may include aparticular deterrent or signal, such as an infrared laser and/or lightoutput, a first entanglement device, or the like. First step may includelocation of a deterrent device; location may include placement in anapparatus 2304. First step may include generation of a deterrentcontrol; generation of a deterrent control system may includetransmission of a signal to initiate deterrent and/or transmission ofany deterrent controls generated as described above, including withoutlimitation transmission of information for localized and/or remotedeterrent control. Transmission may be direct or indirect; for instance,transmission may involve transmission to a remote device that relaystransmission to a deterrent or computing device coupled thereto, ortransmission to an auxiliary computing device or computer memory fortransport to the deterrent and/or computing device coupled thereto.

Referring now to FIG. 26 , an exemplary embodiment of a system 2600 formodifying a deterrent is illustrated. System 2600 includes an automatedthreat detection and deterrence apparatus 2604. Apparatus 2604 mayinclude any computing device as described in this disclosure, includingwithout limitation a microcontroller, microprocessor, digital signalprocessor (DSP) and/or system on a chip (SoC) as described in thisdisclosure. Computing device may include, be included in, and/orcommunicate with a mobile device such as a mobile telephone orsmartphone. Apparatus 2604 may include a single computing deviceoperating independently, or may include two or more computing deviceoperating in concert, in parallel, sequentially or the like; two or morecomputing devices may be included together in a single computing deviceor in two or more computing devices. Apparatus 2604 may interface orcommunicate with one or more additional devices as described below infurther detail via a network interface device. Network interface devicemay be utilized for connecting apparatus 2604 to one or more of avariety of networks, and one or more devices. Examples of a networkinterface device include, but are not limited to, a network interfacecard (e.g., a mobile network interface card, a LAN card), a modem, andany combination thereof. Examples of a network include, but are notlimited to, a wide area network (e.g., the Internet, an enterprisenetwork), a local area network (e.g., a network associated with anoffice, a building, a campus or other relatively small geographicspace), a telephone network, a data network associated with atelephone/voice provider (e.g., a mobile communications provider dataand/or voice network), a direct connection between two computingdevices, and any combinations thereof. A network may employ a wiredand/or a wireless mode of communication. In general, any networktopology may be used. Information (e.g., data, software etc.) may becommunicated to and/or from a computer and/or a computing device.Apparatus 2604 may include but is not limited to, for example, acomputing device or cluster of computing devices in a first location anda second computing device or cluster of computing devices in a secondlocation. Apparatus 2604 may include one or more computing devicesdedicated to data storage, security, distribution of traffic for loadbalancing, and the like. Apparatus 2604 may distribute one or morecomputing tasks as described below across a plurality of computingdevices of computing device, which may operate in parallel, in series,redundantly, or in any other manner used for distribution of tasks ormemory between computing devices. Apparatus 2604 may be implementedusing a “shared nothing” architecture in which data is cached at theworker, in an embodiment, this may enable scalability of system 2600and/or computing device.

Further referring to FIG. 26 , apparatus 2604 may be designed and/orconfigured to perform any method, method step, or sequence of methodsteps in any embodiment described in this disclosure, in any order andwith any degree of repetition. For instance, apparatus 2604 may beconfigured to perform a single step or sequence repeatedly until adesired or commanded outcome is achieved; repetition of a step or asequence of steps may be performed iteratively and/or recursively usingoutputs of previous repetitions as inputs to subsequent repetitions,aggregating inputs and/or outputs of repetitions to produce an aggregateresult, reduction or decrement of one or more variables such as globalvariables, and/or division of a larger processing task into a set ofiteratively addressed smaller processing tasks. Apparatus 2604 mayperform any step or sequence of steps as described in this disclosure inparallel, such as simultaneously and/or substantially simultaneouslyperforming a step two or more times using two or more parallel threads,processor cores, or the like; division of tasks between parallel threadsand/or processes may be performed according to any protocol suitable fordivision of tasks between iterations. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of various waysin which steps, sequences of steps, processing tasks, and/or data may besubdivided, shared, or otherwise dealt with using iteration, recursion,and/or parallel processing.

Still referring to FIG. 26 . apparatus 2604 is configured to identify abehavior 2608 of an individual. As used in this disclosure a “behavior”is an action and mannerism made by an individual, organism, system, orartificial entities in conjunction with themselves or their environment,which includes the other systems or organisms around as well as thephysical environment. As used in this disclosure an “individual” is aperson that exists as a distinct entity, wherein that person possessestheir own behaviors, goals, objectives, and responsibilities. As anon-limiting example, individual 2612 may consist of a 30-year-old male.As a further non-limiting example, individual 2612 may include a48-year-old female. Behavior 2608 may include, without limitation, overtbehavior, wherein overt behavior is a visible type of behavior that canoccur outside of a human being. Overt behavior may include, withoutlimitation, eating food, riding a bicycle, playing football, walking ina secure area, or the like thereof. Behavior 2608 may include, withoutlimitation, covert behavior, wherein covert behavior is not visible toanother individual. Covert behavior may include, without limitation,thoughts, emotions, feelings, or the like thereof. Behavior 2608 mayinclude, without limitation, molecular behavior, wherein molecularbehavior includes unexpected behavior that occurs without thinking,which can be broken down into atomistic parts or molecules. Molecularbehavior may include, without limitation, an individual that closestheir eyes when something is about to interact with that individual'seyes. Behavior 2608 may include, without limitation, molar behavior,wherein molar behavior is a behavior that is identified in terms of theultimate cause of history. Molar Behavior may include, withoutlimitation, a person that loves someone is merely exhibiting a patternof loving behavior over time, as love would be considered atomistic andmust be looked in more wholistic terms. Behavior 2608 may include,without limitation, voluntary behavior, wherein voluntary behavior is atype of behavior that depends on a human want, desire, wish, yearning,or the like thereof. Voluntary behavior may include, without limitation,walking, speaking, writing, striking, and the like thereof. Behavior2608 may include, without limitation, involuntary behavior, whereininvoluntary behavior is a behavior that naturally occurs withoutthinking. Voluntary behavior may include, without limitation, breathing,blinking, swallowing, digestion, or the like thereof. Behavior 2608 mayinclude behavior that is considered to be positive, negative, and/orneutral behavior. As used in this disclosure “positive behavior” isbehavior that is perceived by another individual, organism, artificialintelligence, or entity as a good act. As a non-limiting examplepositive behavior may include altruistic behavior, caring behavior,compassionate behavior, considerate behavior, faithful behavior,impartial behavior, kind behavior, pleasant behavior, polite behavior,sincere behavior, and the like thereof. As used in this closure a“negative behavior” is behavior that is perceived by another individual,organism, artificial intelligence, or entity as a bad act; negativebehavior may include behavior associated with a threat level warrantinga deterrent response for instance as described in U.S. Provisional App.Ser. No. 63/067,142. As a non-limiting example, a negative behavior mayinclude aggressive behavior, argumentative behavior, bossy behavior,deceitful behavior, domineering behavior, flaky behavior, inconsideratebehavior, manipulative behavior, rude behavior, spiteful behavior, andthe like thereof. As used in this disclosure “neutral behavior” isbehavior that is perceived by another individual, organism, artificialintelligence, or entity as a behavior that does not attempt to displayany positive or negative intentions. As a non-limiting example, aneutral behavior may include apathetic behavior, indifferent behavior,behavior indicative of a lack of conviction, or the like.

Still referring to FIG. 26 , behavior 2608 is identified as a functionof at least a datum 2616 that relates to individual. As used in thisdisclosure a “datum” is a piece of information that at least provides aunique quality, trait, and or characteristics about an individual. Forexample, and without limitation datum 2616 may be received by one ormore biometric elements. As used in this disclosure a “biometricelement” is a distinctive, measurable characteristic that at leastlabels and/or identifies an individual. A biometric element may includea physiologic or behavioral characteristic. A physiologicalcharacteristic may relate to the shape and/or structure of theindividual's body. For example, and without limitation a physiologicalcharacteristic may include fingerprint, palm veins, face recognition,DNA, palmprint, hand geometry, iris recognition, retina structure, odor,scent, dental patterns, weight, height, dermal viability, and the likethereof. A behavioral characteristic may relate to the pattern ofbehavior of an individual. A behavioral characteristic may relate to,without limitation, rhythm, gait, voice, typing pattern, typing speed,device use patterns and the like thereof, wherein device use patternsinclude cursor movements, finger pressure, finger contact duration,finger contact volume, finger contact angle, device angle when operatingand the like thereof.

Still referring to FIG. 26 , behavior 2608 may be identified as afunction of a recognition element 2620. As used in this disclosure a“recognition element” is information obtained from one or more sensorsthat relate to a behavior. As a non-limiting example, recognitionelement 2620 may be obtained from one or more sensors relating to avoluntary behavior of an individual entering a secure room, which mayindicate a negative behavior of trespassing. As a further non-limitingexample recognition element 2620 may be obtained from one or moresensors relating to an involuntary behavior of swallowing, which mayindicate a negative behavior of anxiety and/or nervousness. As used inthis disclosure “sensor” is a device that detects or measures a physicalproperty and records, indicates, or otherwise responds to the detectedor measured physical property. Sensors may be comprised of one or moreof imaging and other sensors, such as optical cameras, infrared cameras,3D cameras, multispectral cameras, hyperspectral cameras, polarizedcameras, chemical sensors, motion sensors, ranging sensors, light radarcomponent, such as lidar, detection or imaging using radio frequenciescomponent, such as radar, terahertz or millimeter wave imagers, seismicsensors, magnetic sensors, weight/mass sensors, ionizing radiationsensors, and/or acoustical sensors. Sensors may alternatively oradditionally include any device used as a sensor as described in U.S.Provisional App. Ser. No. 63/067,142.

Still referring to FIG. 26 , behavior 2608 may be identified by one ormore behavior models. Behavior models may include, without limitation,one or more behavior machine learning processes. As used in thisdisclosure “behavior machine-learning process” is a machine-learningprocess that uses training data and/or training set to generate analgorithm that will be performed by an apparatus and/or one or moreremote devices to produce outputs given data provided as inputs; this isin contrast to a non-machine learning software program where thecommands to be executed are determined in advance by a user and writtenin a programming language. A behavior machine-learning process mayinclude any supervised, unsupervised, or reinforcement machine-learningprocess that apparatus 2604 and/or a remote device may or may not use inthe determination of the behavior. A behavior machine-learning processmay include, without limitation machine learning processes such assimple linear regression, multiple linear regression, polynomialregression, support vector regression, ridge regression, lassoregression, elasticnet regression, decision tree regression, randomforest regression, logistic regression, logistic classification,K-nearest neighbors, support vector machines, kernel support vectormachines, naive bayes, decision tree classification, random forestclassification, K-means clustering, hierarchical clustering,dimensionality reduction, principal component analysis, lineardiscriminant analysis, kernel principal component analysis, Q-learning,State Action Reward State Action (SARSA), Deep-Q network, Markovdecision processes, Deep Deterministic Policy Gradient (DDPG), or thelike thereof.

Still referring to FIG. 26 , a behavior machine-learning process may betrained as a function of a behavior training set. As used in thisdisclosure “behavior training set” is a training set that correlates atleast a user action from the plurality of sensors to at least anaccepted standard, wherein a sensor is a device that detects and/ormeasures a physical property of an external surrounding, as describedabove in detail. As used in this disclosure a “user action” is aphysical, psychological, and/or spiritual decision that is acted upon,wherein that action at least impacts one or more surroundingindividuals. For example, and without limitation a user action mayinclude striking an individual, walking into a secure area, coughing onan individual, assaulting an individual, verbally abusing an individual,verbally discriminating against another individuals religious beliefs,sitting on a chair, complimenting an individual, opening a door for anindividual and the like thereof. As used in this disclosure an “acceptedstandard” is one or more ethical standards that are established bysociety to promote trust, fairness and or kindness among a society. Anaccepted standard may include, without limitation, a utilitarianapproach, a rights approach, a justice approach, a common good approach,and/or a virtue approach, as described in detail below in reference toFIG. 27 .

Still referring to FIG. 26 , apparatus 2604 may utilize behavior modeland/or receive the behavior model from a remote device. As used in thisdisclosure a “remote device” is a computing system external to theapparatus that obtains and/or sends information relating to amachine-learning process. Additionally or alternatively, apparatus 2604may receive the behavior model from the remote device. Additionally oralternatively the behavior model may be operated on the remote device,wherein the remote device may determine the behavior and apparatus 2604receives behavior 2608 from the remote device that utilizes one or morebehavior machine learning models. For example, and without limitation, aremote device may provide a behavior to apparatus 2604 using one or morebehavior machine-learning processes, wherein a behavior machine-learningprocess is described above in detail. The remote device may perform thebehavior machine-learning process, using the behavior training set,wherein the behavior training set is described above in detail. Theremote device may transmit a signal, bit, datum, or parameter toapparatus 2604 that at least relates to behavior 2608. Additionally oralternatively, the remote devices may provide modifications to thebehavior machine-learning process. For example, and without limitation,a modification may be comprised of a firmware update, a software update,a behavior machine-learning model correction, and the like thereof. As anon-limiting example a software update may incorporate a new acceptedstandard that relates to a modified user action. Additionally oralternatively, the behavior machine learning process may be transmittedto the remote device, wherein the remote device may update the behaviortraining data and transmit an updated behavior machine-learning processback to apparatus 2604. The updated behavior machine-learning processmay be transmitted by the remote device and received by apparatus 2604as a software update, firmware update, or corrected behaviormachine-learning process.

Still referring to FIG. 26 , a behavior machine-learning process may begenerated as a function of a behavior classifier. A “behaviorclassifier,” as used in this disclosure is a machine-learning model,such as a mathematical model, neural net, or program generated by amachine learning algorithm known as a “classification algorithm,” asdescribed in further detail below, that sorts inputs into categories orbins of data, outputting the categories or bins of data and/or labelsassociated therewith. A behavior classifier may be configured to outputat least a datum that labels or otherwise identifies a set of data thatare clustered together, found to be close under a distance metric asdescribed below, or the like. Apparatus 2604 and/or another device maygenerate a classifier using a classification algorithm, defined as aprocesses whereby apparatus 2604 derives a behavior classifier fromtraining data. Classification may be performed using, withoutlimitation, linear classifiers such as without limitation logisticregression and/or naive Bayes classifiers, nearest neighbor classifierssuch as k-nearest neighbors classifiers, support vector machines, leastsquares support vector machines, fisher's linear discriminant, quadraticclassifiers, decision trees, boosted trees, random forest classifiers,learning vector quantization, and/or neural network-based classifiers.

Still referring to FIG. 26 , apparatus 2604 may be configured togenerate a behavior classifier using a Naïve Bayes classificationalgorithm. Naïve Bayes classification algorithm generates classifiers byassigning class labels to problem instances, represented as vectors ofelement values. Class labels are drawn from a finite set. Naïve Bayesclassification algorithm may include generating a family of algorithmsthat assume that the value of a particular element is independent of thevalue of any other element, given a class variable. Naïve Bayesclassification algorithm may be based on Bayes Theorem expressed asP(A/B)=P(B/A) P(A)÷P(B), where P(A/B) is the probability of hypothesis Agiven data B also known as posterior probability; P(B/A) is theprobability of data B given that the hypothesis A was true; P(A) is theprobability of hypothesis A being true regardless of data also known asprior probability of A; and P(B) is the probability of the dataregardless of the hypothesis. A naive Bayes algorithm may be generatedby first transforming training data into a frequency table. Apparatus2604 may then calculate a likelihood table by calculating probabilitiesof different data entries and classification labels. Apparatus 2604 mayutilize a naive Bayes equation to calculate a posterior probability foreach class. A class containing the highest posterior probability is theoutcome of prediction. Naïve Bayes classification algorithm may includea gaussian model that follows a normal distribution. Naïve Bayesclassification algorithm may include a multinomial model that is usedfor discrete counts. Naïve Bayes classification algorithm may include aBernoulli model that may be utilized when vectors are binary.

With continued reference to FIG. 26 , apparatus 2604 may be configuredto generate a classifier using a K-nearest neighbors (KNN) algorithm. A“K-nearest neighbors algorithm” as used in this disclosure, includes aclassification method that utilizes feature similarity to analyze howclosely out-of-sample-features resemble training data to classify inputdata to one or more clusters and/or categories of features asrepresented in training data; this may be performed by representing bothtraining data and input data in vector forms, and using one or moremeasures of vector similarity to identify classifications withintraining data, and to determine a classification of input data.K-nearest neighbors algorithm may include specifying a K-value, or anumber directing the classifier to select the k most similar entriestraining data to a given sample, determining the most common classifierof the entries in the database, and classifying the known sample; thismay be performed recursively and/or iteratively to generate a classifierthat may be used to classify input data as further samples. Forinstance, an initial set of samples may be performed to cover an initialheuristic and/or “first guess” at an output and/or relationship, whichmay be seeded, without limitation, using expert input received accordingto any process as described herein. As a non-limiting example, aninitial heuristic may include a ranking of associations between inputsand elements of training data. Heuristic may include selecting somenumber of highest-ranking associations and/or training data elements.

With continued reference to FIG. 26 , generating k-nearest neighborsalgorithm may generate a first vector output containing a data entrycluster, generating a second vector output containing an input data, andcalculate the distance between the first vector output and the secondvector output using any suitable norm such as cosine similarity,Euclidean distance measurement, or the like. Each vector output may berepresented, without limitation, as an n-tuple of values, where n is atleast two values. Each value of n-tuple of values may represent ameasurement or other quantitative value associated with a given categoryof data, or attribute, examples of which are provided in further detailbelow; a vector may be represented, without limitation, in n-dimensionalspace using an axis per category of value represented in n-tuple ofvalues, such that a vector has a geometric direction characterizing therelative quantities of attributes in the n-tuple as compared to eachother. Two vectors may be considered equivalent where their directions,and/or the relative quantities of values within each vector as comparedto each other, are the same; thus, as a non-limiting example, a vectorrepresented as [5, 10, 15] may be treated as equivalent, for purposes ofthis disclosure, as a vector represented as [1, 2, 3]. Vectors may bemore similar where their directions are more similar, and more differentwhere their directions are more divergent; however, vector similaritymay alternatively or additionally be determined using averages ofsimilarities between like attributes, or any other measure of similaritysuitable for any n-tuple of values, or aggregation of numericalsimilarity measures for the purposes of loss functions as described infurther detail below. Any vectors as described herein may be scaled,such that each vector represents each attribute along an equivalentscale of values. Each vector may be “normalized,” or divided by a“length” attribute, such as a length attribute l as derived using aPythagorean norm: l=√{square root over (Σ_(i=0) ^(n)a_(i) ²)}, wherea_(i) is attribute number i of the vector. Scaling and/or normalizationmay function to make vector comparison independent of absolutequantities of attributes, while preserving any dependency on similarityof attributes; this may, for instance, be advantageous where casesrepresented in training data are represented by different quantities ofsamples, which may result in proportionally equivalent vectors withdivergent values.

Further referring to FIG. 26 , behavior classifier may include anybehavior classifier as described in as described in U.S. ProvisionalApp. Ser. No. 63/067,142.

Still referring to FIG. 26 , apparatus 2604 is configured to determineat least a deterrent 2624 that impacts behavior 2608. As used in thisdisclosure a “deterrent” is a thing, entity, object, and/or action thatintends to discourage and/or prevent an individual from continuing anaction, behavior, and/or conduct. Deterrent 2624 may include withoutlimitation, directed light, sounds, electrical deterrents,neurostimulators, chemicals, entanglement devices, and the like thereof.As used in this disclosure a “directed light deterrent” is a deterrentthat uses a high-intensity light source such as, but not limited to, alaser, super LED, laser illuminated LED, super-luminescent LED, VCSEL,plasma discharge lamp, and/or high-intensity LED that is actively aimedat and/or focused on individual 2612, to generate a deterrent effect. Asused in this disclosure a “directed sound deterrent” is a sound sourcethat is aimed at individual 2612 in a manner analogous to a directedlight source. A directed sound deterrent may include, without limitationa long-range acoustic device (LRAD), a laser generating localizedplasmas in the atmosphere to create modulating plasmas near theindividual such that audible sound is produced, an ultrasonic carrierwave, and the like thereof. As used in this disclosure “neurostimulationdeterrents” is an electrical source that is projected at individual 2612such that an electrical contact is achieved between an individual andthe deterrent. As a non-limiting example, an electrical shock deterrentmay include a Human Electro-Muscular Incapacitation (HEMI) device, astun gun, a taser, Taser Area Denial System (TADS), a plasma, anelectric field, an ionizer, and the like thereof. As used in thisdisclosure a “chemical deterrent” is a chemical and/or molecule that atleast provide a noxious and/or discomforting experience for individual2612. For example, and without limitation, a chemical deterrent mayinclude pepper spray, malodorant weapons, tear gas, pacifying agent,white phosphorous, aerosolized opioids, and the like thereof. As used inthis disclosure “entanglement devices” are deterrents wherein individual2612 becomes physically trapped in a device and prevents escape of thatindividual. For example, an entanglement device may include, withoutlimitation nets, bolas, and/or other entanglement or entrapment devicesthat are launched ballistically at the individual in order to limit orstop the individual's ability to move normally.

Still referring to FIG. 26 , apparatus 2604 may determine deterrent 2624by identifying candidate deterrents from a deterrent database. As usedin this disclosure “candidate deterrents” are a list and/or group ofdeterrents that at least satisfy the requirements to alter a behavior.For example, and without limitation, candidate deterrents of laser,directed white light, and strobe light pulses may be identified ascandidate deterrents for a behavior of theft. As used in this disclosurea “deterrent database” is a datastore relating to the plurality ofdeterrents an apparatus may or may not have access to utilize. As anon-limiting example candidate deterrents may include a laser, superLED, laser illuminated LED, super-luminescent LED, VCSEL, plasmadischarge lamp, and/or high-intensity LED from an optical deterrentdatabase. Apparatus 2604 may determine a behavior impact valueassociated with the candidate deterrents. As used in this disclosure a“behavior impact value” is a measurable value associated with anintended behavior modification that a deterrent is meant to perform. Forexample, and without limitation a behavior impact value of 20 may bedetermined for a chemical deterrent that has an intended behaviormodification of eliminating a verbally abusive behavior of anindividual. As a further non-limiting example a behavior impact value of2600 may be determined for an entanglement deterrent that has anintended behavior modification of entrapping and ensnaring. As a furthernon-limiting example a behavior impact value of 5 may be determined foran audio output that has an intended behavior modification of cessationof violent and/or damaging actions. As a further non-limiting example abehavior impact value of 72 may be determined for a neurostimulantdeterrent that has an intended behavior modification for alteringnegative behavior to a positive behavior. As a further non-limitingexample a behavior impact value of 92 may be determined for a chemicaldeterrent that has an intended behavior modification for incapacitation.Apparatus may select deterrent 2624 from the candidate deterrents thatat least improves the behavior impact value using a linear programmingalgorithm. A linear programming algorithm may be used to improve thebehavior impact value, wherein apparatus 2604 may use a linear programsuch as, without limitation, a mixed-integer program. A “linearprogram,” as used in this disclosure, is a program that optimizes alinear objective function, given at least a constraint. As anon-limiting example, apparatus 2604 may calculate variables of set ofbehavior impact values of such parameters from goal parameters calculatean output of candidate deterrents using the variables, and select adeterrent having the largest size, according to a given definition of“size,” of the set of deterrent outputs representing the selecteddeterrents; size may, for instance, include absolute value, numericalsize, or the like.

Still referring to FIG. 26 , apparatus 2604 is configured to identify atleast a spatiotemporal element 2628 related to individual 2612 anddeterrent 2616. As used in this disclosure a “spatiotemporal element” isdatum relating to position, velocity, and/or or acceleration of anindividual's physical being. Spatiotemporal element 2628 may beidentified from a sensor of a plurality of sensors, wherein sensors maybe comprised of one or more of imaging and other sensors, chemicalsensors, motion sensors, ranging sensors, light radar component, such aslidar, detection or imaging using radio frequencies component, such asradar, terahertz or millimeter wave imagers, seismic sensors, magneticsensors, weight/mass sensors, ionizing radiation sensors, and/oracoustical sensors, as described above in detail. Spatiotemporal element2628 may include at least a movement of the individual at a specifictime that relates to a deterrent location. As a non-limiting example aspatiotemporal element may include a movement of an individual walkingfrom one non-secure area to another secure area. Spatiotemporal element2628 may be calculated using one or more sensors related to one another.For instance and without limitation, a motion sensor of an individualentering a lobby may be noted, wherein another motion sensor mayindicate that the individual has exited the lobby merely 30 secondlater, indicating a spatiotemporal element of an individual sprintingthrough the lobby and raising the threat level in apparatus 2604. As afurther non-limiting example a plurality of lasers may be utilized todetermine a distance of an individual with respect to time by measuringthe distance the laser emits and calculating the time that is requiredto reflect back to the source. As a further non-limiting example, alight radar component may be utilized to at least determine a distanceof an individual by using one or more Time of Flight (ToF) analyzers,wherein a ToF analyzer transmits a light toward an object or field ofinterest and detects backscattered and/or reflected light, measuring thetotal time that is required to transmit and reflect back to generate adistance. As a Spatiotemporal element 2628 may relate to at least avelocity of the individual at a specific time that relates to adeterrent location. The velocity of an individual may be determined byrapid repeated samples of position and/or doppler effect measurementsusing one or more light radar components and/or audio components. Asused in this disclosure a “deterrent location” is the place and positionof deterrent with respect to an individual. For example, and withoutlimitation a deterrent location may consist of a corner of a roomopposite to an entryway at an elevation of 10 feet. As a furthernon-limiting example a deterrent location may include a deterrent on thehandle of the door that provides entry into a secure location. Forexample, and without limitation, a spatiotemporal element may determinethat individual 2612 is 10 feet from the chemical deterrent location,while 5 feet away from the entanglement deterrent location. As a furthernon-limiting example, a spatiotemporal element may determine thatindividual 2612 is 15 feet from a directed light deterrent location andmoving at an angle of 5 degrees perpendicular to the deterrent lightsource with a velocity of 5 feet per second.

Still referring to FIG. 26 , apparatus 2604 is configured to generate asafety modifier 2632 as a function of spatiotemporal element 2628. Asused in this disclosure a “safety modifier” is a function thatestablishes one or more constraints for deterrent elements. As used inthis disclosure “constraints” are limits that a deterrent element maynot exceed or otherwise violate, such that the deterrent elementoperates safely. As used in this disclosure “deterrent elements” areparameters according to which the deterrents can be altered and/ormodified. For example, and without limitation deterrent elements mayinclude energy output, energy intensity, energy duty cycle, energyon/off cycle, energy limiter, audio quality, audio intensity, audioduration, audio pulse, audio location, light intensity, light dutycycle, light pulse, light location, light color, laser intensity, laserduty cycle, laser pulse, laser wavelength, current, voltage, wattage,and the like thereof. As a non-limiting example a constraint may includean upper limit of 5 volts for an electrical deterrent and a lower limitof 2 volts for an electrical deterrent. Additionally or alternatively, asafety modifier may indicate that a deterrent element of energy outputmay be 200 joules, while the energy on/off cycle may only be 1millisecond. As a further non-limiting example, a safety modifier mayindicate that a deterrent element of ballistic speed must be within therange of 5-10 m/s.

Still referring to FIG. 26 , apparatus 2604 generates safety modifier2632 by identifying a distance parameter 2636 as a function ofspatiotemporal element 2632. As used in this disclosure a “distanceparameter” is a set length that exists amongst the individual and thedeterrent location. For example, and without limitation, a distanceparameter of 10 feet may be identified of ran individual that hasentered a secure location, wherein the deterrent location exists 10 feetaway from the individual at the entryway. Distance parameter 2636 may bealtered as the individual moves towards or away from the deterrentlocation. Apparatus 2604 determines a velocity parameter 2640 as afunction of spatiotemporal element 2628. As used in this disclosure a“velocity parameter” is a calculated speed and vector that existsamongst the individual and the deterrent location. For example, andwithout limitation, a velocity parameter may be identified as a speed of10 m/s in the direction 30 degrees northwest. As a further non-limitingexample, a velocity parameter may indicate that an individual is movingat a speed of 8 m/s towards the deterrent location. As a furthernon-limiting example a velocity parameter may indicate that anindividual is fleeing from a secure area at a speed of 15 m/s,indicating theft from the secure area. Safety modifier 2632 is generatedas a function of distance parameter 2636 and the velocity parameter2640. For example, and without limitation, an individual may betrespassing in a secure area wherein the distance parameter indicatesthat the individual is 5 feet from the deterrent location and thevelocity parameter indicates that the individual is moving away from thedeterrent location at a speed of 1 m/s, wherein the safety modifier mayindicate that an audio output be limited to an intensity of 25-50%. As afurther non-limiting example, an individual may be fleeing from a theftwherein the distance parameter indicates that the individual is 20 feetfrom the deterrent location and the velocity parameter indicates thatthe individual is moving away from the deterrent location at a speed of10 m/s, wherein the safety modifier may indicate that directed lightsource be enhanced to a laser power of 2,000 joules with an intensity of95%.

Still referring to FIG. 26 , safety modifier 2632 may be generated bydetermining at least a deterrent impact. As used in this disclosure a“deterrent impact” is an effect an external stimulus has on a user. Forexample, and without limitation, a deterrent impact may include coveringof ears to attempt to block out an audio signal as a result of anexternal stimulus of a 10,000 Hz audio signal. A deterrent impact may becomprised of a measurable element associated with an effect thedeterrent has on a user. A deterrent impact may provide an overall valueassociated with the likelihood that an individual may or may not beharmed or otherwise negatively impacted. As a non-limiting example adeterrent impact may be comprised of a value of 10 indicating a strongpropensity for causing dermal burns on an individual. Additionally oralternatively, a deterrent impact may identify one or more impactsassociated with an external stimulus. As a non-limiting example adeterrent impact may identify an impact of temporary blindness, damageto vision, temporary incapacitation, nausea, seizures, post-traumaticstress disorder and/or retinal burning associated with an externalstimulus of a laser. As a further non-limiting example a deterrentimpact may identify an impact of hearing loss, tinnitus, sensorineuralhearing loss, conductive hearing loss, post-traumatic stress disorder,auditory nerve damage, and/or ruptured eardrums associated with anexternal stimulus of an audio signal. As a further non-limiting examplea deterrent impact may identify an impact of loss of consciousness,muscle spasms, numbness, tingling, breathing disorders, headache,electrical burns, heart arrythmias, and/or tetanus associated with anexternal stimulus of an electrical shock. As a further non-limitingexample a deterrent impact may identify an impact of incapacitation,nerve damage, muscle contraction, nausea, headache, stomachache, and/orseizures associated with an external stimulus of a neurostimulant. As afurther non-limiting example a deterrent impact may identify an impactof incapacitation, cross-reactivity, burning sensation, permanent nervedamage, nausea, vomiting, and/or blindness associated with an externalstimulus of a chemical. As a further non-limiting example a deterrentimpact may identify an impact of muscle contraction, blunt force trauma,loss of limb, and/or puncture wound associated with an external stimulusof an entanglement device.

Still referring to FIG. 26 , apparatus 2604 may determine deterrentimpact as a function of deterrent location. The deterrent impact may bedecrease as a function of a deterrent location that is far away, whereina deterrent impact may increase as a function of a deterrent locationthat is close. As a non-limiting example a directed light deterrent mayresult in a beam spread, wherein beam spread is may include decreasingintensity per area of light as distance increase and/or aninverse-square law for point sources. As a further non-limiting exampleaudio signals and/or chemicals may follow an inverse-square law, whereinas the deterrent location increases the deterrent impact will decrease.Additionally or alternatively, a deterrent impact may be avoided due toa threshold limit that at least identifies a deterrent that is deemed toinvasive due to a short deterrent location, wherein a threshold is agiven limit that a deterrent impact may not exceed as a function of adeterrent location. For example, and without limitation, a deterrentlocation of 3 cm, which may result in a deterrent impact of permanentretinal damage, may violate a threshold of 1 m for a directed lightdeterrent. As a further non-limiting example a deterrent location of 1mm, which may result in a deterrent impact of death, may violate athreshold of 10 m for a chemical deterrent. Additionally oralternatively, deterrents may be selected due to the reduced deterrentlocation, as the deterrent impact may decrease due to a deterrentlocation being larger. As a non-limiting example an electrical shockdeterrent may require a deterrent location to be less than 5 cm or thedeterrent impact may result in no impact, or limited impact.

Still referring to FIG. 26 , apparatus 2604 may determine deterrentimpact as a function of one or more future positions of an individual.As used in this disclosure “future positions” are locations of anindividual that are calculated using one or more spatiotemporalelements, to at least determine the position of the individual at thetime of the deterrent impact. The future positions may be calculatedusing one or more kinematic equations for constant acceleration usingintegral calculus, the integral formulation of the kinematics equationsin analyzing motion, the functional form of velocity verses time giventhe acceleration function, and/or the functional form of position versustime given the velocity function. Apparatus 2604 may identify anindividual's future position using one or more heuristic algorithms. Asused in this disclosure “heuristic algorithms” are algorithms thatenhance speed, and/or decrease complexity, of calculations whilesacrificing relatively little accuracy, such that an output such as afuture position may be rapidly determined with prior to an individualmovement. In an embodiment, using at least a heuristic algorithms todetermine a future position may drastically reduce the time needed toperform the determination, while reducing a miniscule amount ofaccuracy; for example heuristic determination of future positions may beseveral factors of ten faster than brute force approaches. In otherembodiments, heuristic algorithms may include a heuristic algorithmbased on a single variable or a small set of variables, such as a singlevelocity and/or location.

Still referring to FIG. 26 , apparatus 2604 may determine deterrentidentify using one or more position machine-learning models. As used inthis disclosure a “position machine-learning model” is amachine-learning model to produce a deterrent impact output givendeterrent locations provided as inputs; this is in contrast to anon-machine learning software program where the commands to be executedare determined in advance by a user and written in a programminglanguage. Position machine-learning model may include one or moreposition machine-learning processes such as supervised, unsupervised, orreinforcement machine-learning processes that apparatus 2604 and/or aremote device may or may not use in the determination of the deterrentimpact. A position machine-learning process may include, withoutlimitation machine learning processes such as simple linear regression,multiple linear regression, polynomial regression, support vectorregression, ridge regression, lasso regression, elasticnet regression,decision tree regression, random forest regression, logistic regression,logistic classification, K-nearest neighbors, support vector machines,kernel support vector machines, naive bayes, decision treeclassification, random forest classification, K-means clustering,hierarchical clustering, dimensionality reduction, principal componentanalysis, linear discriminant analysis, kernel principal componentanalysis, Q-learning, State Action Reward State Action (SARSA), Deep-Qnetwork, Markov decision processes, Deep Deterministic Policy Gradient(DDPG), or the like thereof.

Still referring to FIG. 26 , a position machine-learning process may betrained as a function of a position training set. As used in thisdisclosure a “position training set” is a training set that correlatesat least a deterrent location to at least an invasive parameter, whereina deterrent impact includes all of deterrent impact as described above.As used in this disclosure an “invasive parameter” is a variableassociated with the magnitude of invasiveness that a deterrent exerts onan individual. For example, and without limitation, a position trainingset may relate a deterrent location of 5 mm with an invasive parameterof 40 for incapacitation using a ballistic deterrent. As a furthernon-limiting example, a position training set may relate a deterrentlocation of 10 m with an invasive parameter of 2 for incapacitationusing an audio output deterrent. The position training set may bereceived from one or more remote devices, wherein apparatus 2604 mayreceive one or more position training set updates. A position trainingset update may include one or more software updates, firmware updates,signals, bits, data, and/or parameters that at least relates a deterrentlocation to an at least invasive parameter. The position training setmay be generated as a function of apparatus 2604 previous experiences,wherein the deterrent location may be related to the invasive parameterand stored as a position training set.

Still referring to FIG. 26 , apparatus 2604 may generate safety modifier2632 using the deterrent impact, spatiotemporal element 2628, and atleast a safety machine model. As used in this disclosure a “safetymachine-learning model” is a machine-learning model to produce a safetymodifier output given distance parameters, velocity parameters,acceleration parameters, and/or additional spatiotemporal elementsprovided as inputs; this is in contrast to a non-machine learningsoftware program where the commands to be executed are determined inadvance by a user and written in a programming language. Safetymachine-learning model may include one or more safety machine-learningprocesses such as supervised, unsupervised, or reinforcementmachine-learning processes that apparatus 2604 and/or a remote devicemay or may not use in the determination of the behavior. A safetymachine-learning process may include, without limitation machinelearning processes such as simple linear regression, multiple linearregression, polynomial regression, support vector regression, ridgeregression, lasso regression, elasticnet regression, decision treeregression, random forest regression, logistic regression, logisticclassification, K-nearest neighbors, support vector machines, kernelsupport vector machines, naive bayes, decision tree classification,random forest classification, K-means clustering, hierarchicalclustering, dimensionality reduction, principal component analysis,linear discriminant analysis, kernel principal component analysis,Q-learning, State Action Reward State Action (SARSA), Deep-Q network,Markov decision processes, Deep Deterministic Policy Gradient (DDPG), orthe like thereof.

Still referring to FIG. 26 , a safety machine-learning process may betrained as a function of a safety training set. As used in thisdisclosure a “safety training set” is a training set that correlates atleast a deterrent impact to at least a spatiotemporal element, wherein adeterrent impact and spatiotemporal element includes all of deterrentimpact and spatiotemporal element as described above. For example, andwithout limitation, a safety training set may relate a deterrent impactof incapacitation with a spatiotemporal element of 5 feet from adeterrent location, wherein the individual is walking away from thedeterrent location at a speed of 1 m/s. As a further non-limitingexample, a safety training set may relate a deterrent impact of bluntforce trauma to a spatiotemporal element of 20 feet from a deterrentlocation, wherein the individual is sprinting towards the deterrentlocation at a speed of 15 m/s. As a further non-limiting example, asafety training set may relate a deterrent impact of temporarydiscomfort to a spatiotemporal element of an individual 50 feet from adeterrent location walking away from the deterrent location at a speedof 10 m/s.

Still referring to FIG. 26 , apparatus 2604 may receive the safetymachine-learning process from a remote device that utilizes one or moresafety machine learning processes, wherein a remote device is describedabove in detail. For example, and without limitation, a remote devicemay include a computing device, external device, processor, and the likethereof. The remote device may perform the safety machine-learningprocess using safety training set to generate safety modifier 2632 andtransmit the output to apparatus 2604. The remote device may transmit asignal, bit, datum, or parameter to apparatus 2604 that at least relatesto safety modifier 2632. Additionally or alternatively, the remotedevice may provide an updated machine-learning process. For example, andwithout limitation, an updated machine-learning process may be comprisedof a firmware update, a software update, a safety machine-learning modelcorrection, and the like thereof. As a non-limiting example a softwareupdate may incorporate a new deterrent impact that relates to a modifiedspatiotemporal element. Additionally or alternatively, the updatedmachine learning process may be transmitted to the remote device,wherein the remote device may replace the safety machine-learningprocess with the updated machine-learning process and determine thesafety modifier as a function of the spatiotemporal element using theupdated machine-learning process. The updated machine-learning processmay be transmitted by the remote device and received by apparatus 2604as a software update, firmware update, or corrected safetymachine-learning process. For example, and without limitation a safetymachine-learning process may utilize neural net algorithms, wherein theupdated machine-learning process may incorporate polynomial regressionalgorithms.

Still referring to FIG. 26 , apparatus 2604 may modify deterrent 2624 bydetermining at least a deterrent element of a plurality of deterrentelements. Deterrent elements include parameters according to which thedeterrent component can be altered and/or modified, as described abovein detail. Apparatus 2604 may determine a deterrent element that atleast differs from safety modifier 2632 using a modifying algorithm. Asused in this disclosure a “modifying algorithm” is a mathematicalformula that at least relates a value to another value and/or range ofvalues. As a non-limiting example, a modifying algorithm may includeaddition formulas, subtraction formulas, lattice formulas, scratchformulas, and the like thereof. The deterrent element may control one ormore deterrent outputs. As used in this disclosure a “deterrent output”is the object, and or matter that is emitted from the deterrentcomponent. For example, a deterrent output may include, withoutlimitation, a laser, light, net, bola, chemical, current, and the likethereof. As a non-limiting example a deterrent element may include thepower, duty cycle, intensity, pulse time, current, wattage, voltage,concentration, wavelength, ballistic force, ballistic velocity,ballistic acceleration, and the like thereof. Deterrent output may behalted due to one or more thresholds, wherein a threshold is a givenlimit that a deterrent output may not exceed. As a non-limiting examplea deterrent output of 80% may exceed the threshold of 70%, wherein thedeterrent output may be stopped due to exceeding the threshold.

Still referring to FIG. 26 , apparatus 2604 is configured to initiate amodified deterrent 2644. Apparatus may initiate modified deterrent 2644including a deterrent of an infrared laser and/or light output, a firstentanglement device, or the like. Apparatus 2604 may locate modifieddeterrent 2644; location may include placement in an apparatus 2604,placement in proximity to apparatus 2604, and/or placement external toapparatus 2604. Apparatus 2604 may generate deterrent control;generation of a deterrent control system may include transmission of asignal to initiate deterrent and/or transmission of any deterrentcontrols generated as described above, including without limitationtransmission of information for localized and/or remote deterrentcontrol. Transmission may be direct or indirect; for instance,transmission may involve transmission to a remote device that relaystransmission to a deterrent or computing device coupled thereto, ortransmission to an auxiliary computing device or computer memory fortransport to the deterrent and/or computing device coupled thereto.

Referring now to FIG. 27 , an exemplary embodiment of 2700 an ethicaldatabase 2704 according to an embodiment of the invention isillustrated. Ethical database 2704 may be implemented, withoutlimitation, as a relational database, a key-value retrieval databasesuch as a NOSQL database, or any other format or structure for use as adatabase that a person skilled in the art would recognize as suitableupon review of the entirety of this disclosure. Database mayalternatively or additionally be implemented using a distributed datastorage protocol and/or data structure, such as a distributed hash tableor the like. Database may include a plurality of data entries and/orrecords as described above. Data entries in a database may be flaggedwith or linked to one or more additional elements of information, whichmay be reflected in data entry cells and/or in linked tables such astables related by one or more indices in a relational database. Personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various ways in which data entries in a database may store,retrieve, organize, and/or reflect data and/or records as used herein,as well as categories and/or populations of data consistently with thisdisclosure. Ethical database 2704 may include one or more tables,including without limitation, a utilitarian tableset 2708; utilitariantableset 2708 may include actions that are assessed in terms of itsconsequences and/or outcomes and strives. As a non-limiting example anindividual that is displaying behavior that is selfish, wherein theoutcome is personal gain, may be identified as a negative acceptedstandard. Ethical database 2704 may include one or more tables,including without limitation, a rights tableset 2712; rights tableset2712 may include actions that best protect and respect the moral rightsof individuals. As a non-limiting example moral rights may include,without limitation, rights to make one's own choices, to be told thetruth, not to be injured, a degree of privacy, and the like thereof.Ethical database 2704 may include one or more tables, including withoutlimitation, a justice tableset 2716; justice tableset 2716 may includeactions that relate to equality and/or justice. As a non-limitingexample actions such as discrimination and/or favoritism may not beconsidered equality behaviors, whilst actions of impartiality and/orconsiderate may be considered justice behaviors. Ethical database may2704 may include one or more tables, including without limitation, acommon good tableset 2720; common good tableset 2720 may include actionsthat are taken in order to benefit not only a certain group ofindividuals, but the society as a whole. As a non-limiting exampleactions of an individual tampering with a building may denote a negativebehavior, while actions of cleaning a public hallway or removing litterfrom a secure area may consist of a positive behavior. Ethical database2704 may include one or more tables, including without limitation, avirtue tableset 2724; virtue tableset 2724 may include actions that aretaken in order to achieve a full development of our humanity. As anon-limiting example actions relating to honesty, courage, compassion,generosity, tolerance, love, fidelity, integrity, fairness,self-control, and prudence may all be considered virtues that may aid inachieving full development of an individual's humanity.

Referring now to FIG. 28 , an exemplary embodiment of 2800 a deterrentdatabase 2804 according to an embodiment of the invention isillustrated. Deterrent database 2804 may be implemented, withoutlimitation, as a relational database, a key-value retrieval databasesuch as a NOSQL database, or any other format or structure for use as adatabase that a person skilled in the art would recognize as suitableupon review of the entirety of this disclosure. Database mayalternatively or additionally be implemented using a distributed datastorage protocol and/or data structure, such as a distributed hash tableor the like. Database may include a plurality of data entries and/orrecords as described above. Data entries in a database may be flaggedwith or linked to one or more additional elements of information, whichmay be reflected in data entry cells and/or in linked tables such astables related by one or more indices in a relational database. Personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various ways in which data entries in a database may store,retrieve, organize, and/or reflect data and/or records as used herein,as well as categories and/or populations of data consistently with thisdisclosure. Deterrent database may 2804 may include one or more tables,including without limitation, a light tableset 2808; light tableset 2808may include deterrents that use a high-intensity light source that isactively aimed at and/or focused on an individual. As a non-limitingexample light tableset may include, without limitation, one or moreelements of a laser, a high intensity Eli D, a beam expander, a freebream spreader, a focusing optic, microwave source, and the likethereof. Deterrent database 2804 may include one or more tables,including without limitation, a sound tableset 2812; sound tableset 2812may include deterrents that direct sound sources to individuals in amanner analogous to a directed light source. As a non-limiting example,sound tableset 2812 may include, without limitation, a long-rangeacoustic device (LRAD), an ultrasonic carrier, multiple LRAD speakers,and the like thereof. Deterrent database 2804 may include one or moretables, including without limitation, an electrical tableset 2816;electrical tableset 2816 may include deterrents that apply at least anelectrical current, voltage, and/or wattage to an individual. As anon-limiting example, electrical tableset 2816 may include a “stun gun”,taser, and or shock device that generates a shock upon contact.Deterrent database may 2804 may include one or more tables, includingwithout limitation, a neurostimulant tableset 2820; neurostimulanttableset 2820 may include deterrents that apply at least a stimulus thatcauses discomfort and/or neurological impairment, such as pre-epilepticeffects. As a non-limiting example, neurostimulant tableset 2820 mayinclude microelectrodes, transcranial electric stimulators, magneticfields, strobe effect lights, and or electromagnetic neuromodulators.Deterrent database 2804 may include one or more tables, includingwithout limitation, a chemical tableset 2824; chemical tableset 2824 mayinclude deterrents that release one or more chemicals to at least altera behavior of an individual. As a non-limiting example, chemicaltableset 2824 may include pepper spray, “skunk” weapons, tear gas,irritants, noxious gas, and the like thereof. Deterrent database 2804may include one or more tables, including without limitation, anentanglement tableset 2828; entanglement tableset 2828 may includedeterrents that fire ballistics at an individual such that theindividual's ability to move is stopped and/or limited. As anon-limiting example, entanglement tableset 2828 may include, withoutlimitation, nets, bolas, and the like thereof.

Now referring to FIG. 29 , an exemplary embodiment of a method 2900 formodifying a deterrent is illustrated. At step 2905, an apparatus 2404identifies a behavior 2608 of an individual 2612. Behavior 2608 may beimplemented, without limitation, as described above in reference toFIGS. 1-4 . Individual 2612 may be implemented, without limitation, asdescribed above in reference to FIGS. 1-4 . Behavior 2608 is identifiedas a function of one or more datum 2616 relating to individual 2612.Datum 2616 may be implemented, without limitation, as described above inreference to FIGS. 1-4 . Behavior 2608 may be identified as a functionof a recognition element 2620. Recognition element 2620 may beimplemented, without limitation, as described above in reference toFIGS. 1-4 . Behavior 2608 may be identified as a function of a behaviormachine-learning model that is performed by apparatus 2604 and/or one ormore remote devices. The behavior machine-learning model may beimplemented, without limitation, as described above in reference toFIGS. 1-4 . The behavior machine-learning model may be configured usinga behavior training set. The behavior training set may be implemented,without limitation, as described above in reference to FIGS. 1-4 .

Still referring to FIG. 29 , at step 2910, apparatus 2604 determines adeterrent 2624 that at least impacts behavior 2608. Deterrent 2624 maybe implemented, without limitation, as described above in reference toFIGS. 1-4 . Deterrent 2624 may be identified by identifying one or morecandidate deterrents from a deterrent database 2804. Candidatedeterrents may be implemented, without limitation, as described above inreference to FIGS. 1-4 . Deterrent database may be implemented, withoutlimitation, as described above in reference to FIGS. 1-4 .

Still referring to FIG. 29 , at step 2915, apparatus 2604 identifies atleast a spatiotemporal element 2628 related to individual 2612 anddeterrent 2624. Spatiotemporal element 2628 may be implemented, withoutlimitation, as described above in reference to FIGS. 1-4 .Spatiotemporal element may be identified from a sensor of a plurality ofsensors, wherein sensors may be implemented, without limitation, asdescribed above in reference to FIGS. 1-4 .

Still referring to FIG. 29 , at step 2920, apparatus 2604 generates asafety modifier 2632 as a function of spatiotemporal element 2628.Safety modifier 2632 may be implemented, without limitation, asdescribed above in reference to FIGS. 1-4 . Safety modifier 2632 mayindicate one or more constraints for deterrent elements. Deterrentelements may be implemented, without limitation, as described above inreference to FIGS. 1-4 . Safety modifier 2632 is generated byidentifying a distance parameter 2636 as a function of spatiotemporalelement 2628. Distance parameter may be implemented, without limitation,as described above in reference to FIGS. 1-4 . Safety modifier 2632 isgenerated by determining a velocity parameter 2640 as a function ofspatiotemporal element 2628. Velocity parameter 2640 may be implemented,without limitation, as described above in reference to FIGS. 1-4 .Safety modifier 2632 may be generated by determining at least adeterrent impact, wherein a deterrent impact may be implemented, withoutlimitation, as described above in reference to FIGS. 1-4 . Safetymodifier 2632 may be generated using the deterrent impact,spatiotemporal element 2628, and at least a safety machine-learningmodel that is performed by apparatus 2604 and/or one or more remotedevices. The safety machine-learning model may be implemented, withoutlimitation, as described above in reference to FIGS. 1-4 . The safetymachine-learning model may be configured using a safety training set.The safety training set may be implemented, without limitation, asdescribed above in reference to FIGS. 1-4 .

Still referring to FIG. 29 , at step 2925, apparatus 2604 modifiesdeterrent 2624 as a function of safety modifier 2632. Deterrent 2624 maybe modified by receiving a deterrent space relating to the plurality ofdeterrent elements associated with deterrent 2624, wherein deterrentspace may be implemented, without limitation, as described above inreference to FIGS. 1-4 . Apparatus 2604 may modify the deterrent elementthat at least differs from safety modifier 2632 using a modifyingalgorithm, wherein a modifying algorithm may be implemented, withoutlimitation, as described above in reference to FIGS. 1-4 . Apparatus maymodify the deterrent using the deterrent element to at least alter thedeterrent output, wherein the deterrent output may be implemented,without limitation, as described above in reference to FIGS. 1-4 .

Still referring to FIG. 29 , at step 2930, apparatus 2604 initiates amodified deterrent 2644. Modified deterrent 2644 may be implemented,without limitation, as described above in reference to FIGS. 1-4 .Initiation of modified deterrent 2644 may include performance of a firststep in the initiation of a modified deterrent; first step may include aparticular modified deterrent or signal, such as an infrared laserand/or light output, a first entanglement device, or the like. Firststep may include location of a modified deterrent device; location mayinclude placement in an apparatus 2604. First step may includegeneration of a modified deterrent control; generation of a modifieddeterrent control system may include transmission of a signal toinitiate deterrent and/or transmission of any modified deterrentcontrols generated as described above, including without limitationtransmission of information for localized and/or remote deterrentcontrol. Transmission may be direct or indirect; for instance,transmission may involve transmission to a remote device that relaystransmission to a deterrent or computing device coupled thereto, ortransmission to an auxiliary computing device or computer memory fortransport to the deterrent and/or computing device coupled thereto.

Referring now to FIG. 30 , an exemplary embodiment of a system 3000 foraltering an individual behavior is illustrated. System includes anapparatus 3004. Apparatus 3004 may include any computing device asdescribed in this disclosure, including without limitation amicrocontroller, microprocessor, digital signal processor (DSP) and/orsystem on a chip (SoC) as described in this disclosure. Computing devicemay include, be included in, and/or communicate with a mobile devicesuch as a mobile telephone or smartphone. Apparatus 3004 may include asingle computing device operating independently, or may include two ormore computing device operating in concert, in parallel, sequentially orthe like; two or more computing devices may be included together in asingle computing device or in two or more computing devices. Apparatus3004 may interface or communicate with one or more additional devices asdescribed below in further detail via a network interface device.Network interface device may be utilized for connecting apparatus 3004to one or more of a variety of networks, and one or more devices.Examples of a network interface device include, but are not limited to,a network interface card (e.g., a mobile network interface card, a LANcard), a modem, and any combination thereof. Examples of a networkinclude, but are not limited to, a wide area network (e.g., theInternet, an enterprise network), a local area network (e.g., a networkassociated with an office, a building, a campus or other relativelysmall geographic space), a telephone network, a data network associatedwith a telephone/voice provider (e.g., a mobile communications providerdata and/or voice network), a direct connection between two computingdevices, and any combinations thereof. A network may employ a wiredand/or a wireless mode of communication. In general, any networktopology may be used. Information (e.g., data, software etc.) may becommunicated to and/or from a computer and/or a computing device.Apparatus 3004 may include but is not limited to, for example, acomputing device or cluster of computing devices in a first location anda second computing device or cluster of computing devices in a secondlocation. Apparatus 3004 may include one or more computing devicesdedicated to data storage, security, distribution of traffic for loadbalancing, and the like. Apparatus 3004 may distribute one or morecomputing tasks as described below across a plurality of computingdevices of computing device, which may operate in parallel, in series,redundantly, or in any other manner used for distribution of tasks ormemory between computing devices. Apparatus 3004 may be implementedusing a “shared nothing” architecture in which data is cached at theworker, in an embodiment, this may enable scalability of system 3000and/or computing device.

Apparatus 3004 may be designed and/or configured to perform any method,method step, or sequence of method steps in any embodiment described inthis disclosure, in any order and with any degree of repetition. Forinstance, apparatus 3004 may be configured to perform a single step orsequence repeatedly until a desired or commanded outcome is achieved;repetition of a step or a sequence of steps may be performed iterativelyand/or recursively using outputs of previous repetitions as inputs tosubsequent repetitions, aggregating inputs and/or outputs of repetitionsto produce an aggregate result, reduction or decrement of one or morevariables such as global variables, and/or division of a largerprocessing task into a set of iteratively addressed smaller processingtasks. Apparatus 3004 may perform any step or sequence of steps asdescribed in this disclosure in parallel, such as simultaneously and/orsubstantially simultaneously performing a step two or more times usingtwo or more parallel threads, processor cores, or the like; division oftasks between parallel threads and/or processes may be performedaccording to any protocol suitable for division of tasks betweeniterations. Persons skilled in the art, upon reviewing the entirety ofthis disclosure, will be aware of various ways in which steps, sequencesof steps, processing tasks, and/or data may be subdivided, shared, orotherwise dealt with using iteration, recursion, and/or parallelprocessing.

Still referring to FIG. 30 , apparatus 3004 is configured to receive aplurality of sensor data 3008. As used in this disclosure “sensor data”is information that relates to one or more external physical elements ascaptured by one or more sensors; one or more sensors may include adevice that detects a physical property such as a light sensor, acousticsensor, chemical sensor, force sensor, pressure sensor, temperaturesensor, humidity sensor, gyroscopic sensor, proximity sensor, flowsensor, image sensor, magnetic sensor, and the like thereof. As used inthis disclosure a “physical element” is a physical property thatrepresents at least an entity, matter, and/or object. For example, andwithout limitation a physical element may include, without limitation, alight, voltage, current, sound, chemical, pressure, humidity, and thelike thereof. For example, and without limitation, sensory data 3008 maybe comprised of a pressure datum of 0.9821 atm. As a furthernon-limiting example, sensor data 3008 may be comprised of a chemicaldatum of 3100 ppb of N-Phenethyl-4-piperidone. As a further non-limitingexample sensor data 3008 may include an image of a secure area, whichmay include an individual that is present in the secure area. As used inthis disclosure “sensor” is a device that detects or measures a physicalproperty and records, indicates, or otherwise responds to the detectedor measured physical property. Sensors may be comprised of one or moreof imaging and other sensors, such as optical cameras, infrared cameras,3D cameras, multispectral cameras, hyperspectral cameras, polarizedcameras, chemical sensors, motion sensors, ranging sensors, light radarcomponent, such as lidar, detection or imaging using radio frequenciescomponent, such as radar, terahertz or millimeter wave imagers, seismicsensors, magnetic sensors, weight/mass sensors, ionizing radiationsensors, and/or acoustical sensors. Sensors may alternatively oradditionally include any device used as a sensor as described in U.S.Provisional App. Ser. No. 63/067,142.

Still referring to FIG. 30 , apparatus 3004 may receive a recognitionelement from a plurality of sensors. As used in this disclosure a“recognition element” is information obtained from one or more sensorsthat relate to an individual. As a non-limiting example, recognitionelement may consist of a facial feature such as eyes, nose, mouth, cheekbones, smile, and the like thereof. As a further non-limiting example, arecognition element may include a biometric element relating to theindividual. As used in this disclosure a “biometric element” is adistinctive, measurable characteristic that at least labels and/oridentifies an individual. A biometric element may include a physiologiccharacteristic. A physiological characteristic may relate to the shapeand/or structure of the individual's body. For example, and withoutlimitation a physiological characteristic may include fingerprint, palmveins, face recognition, DNA, palmprint, hand geometry, irisrecognition, retina structure, odor, scent, dental patterns, weight,height, dermal viability, and the like thereof. As a furthernon-limiting example a recognition element may relate to an individual'srhythm, gait, voice, typing pattern, typing speed, device use patternsand the like thereof, wherein device use patterns include cursormovements, finger pressure, finger contact duration, finger contactvolume, finger contact angle, device angle when operating and the likethereof. As used in this disclosure an “individual” is. As used in thisdisclosure an “individual” is a person that exists as a distinct entity,wherein that person possesses their own behaviors, goals, objectives,and responsibilities. As a non-limiting example, an individual mayconsist of a 30-year-old male. As a further non-limiting example, anindividual may include a 48-year-old female.

Still referring to FIG. 30 , apparatus 3004 may obtain an identificationelement of an individual from an identification database. As used inthis disclosure an identification element is datum and/or quality thatat least uniquely defines an individual. For example, and withoutlimitation an identification element may include a security clearance, aname, an identification number, and the like thereof. As used in thisdisclosure a “security clearance” is a status granted to individualsallowing them access to classified information or to restricted areas,after completion of a thorough background check. For example, andwithout limitation a security clearance may include access to a level 5secure area, wherein there a total of 10 levels of security. As used inthis disclosure a “name” is a word or set of words by which anindividual is known, addressed, or referred to. For example, and withoutlimitation a name may include common names, such as John, James, Robert,Michael, William, David, Richard, Mary, Patricia, Linda, Barbara.Elizabeth, Jennifer, and the like thereof. As used in this disclosure anidentification number is any number or set of numbers by which anindividual may be identified. For example, and without limitation, a setof numbers may include social security number, telephone number, date ofbirth, residence zip code, and the like thereof. As used in thisdisclosure an “identification database” is a databank that at leaststores, retains, and/or maintains identification elements ofindividuals. For example, and without limitation, identificationdatabases may include a National ID card, passport, social securitydeath index, mail isolation control and tracking, integrated automatedfingerprint identification system, combined DNA index system,investigate data warehouse, project MINARET watch lists, NSA calldatabase, TALON, Homeless Management Information Systems, CaseManagement and Electronic Case Files, and the like thereof.

Still referring to FIG. 30 , apparatus 3004 may relate the recognitionelement to the identification element using a recognition model. As usedin this disclosure “recognition model” is a machine-learning model thatuses training data and/or training set to generate an algorithm thatwill be performed by an apparatus and/or one or more remote devices toproduce outputs given data provided as inputs; this is in contrast to anon-machine learning software program where the commands to be executedare determined in advance by a user and written in a programminglanguage. A recognition model may include any supervised, unsupervised,or reinforcement machine-learning process that apparatus 3004 and/or oneor more remotes devices may or may not use in the identification of anindividual. A recognition model may include, without limitation machinelearning processes such as simple linear regression, multiple linearregression, polynomial regression, support vector regression, ridgeregression, lasso regression, elasticnet regression, decision treeregression, random forest regression, logistic regression, logisticclassification, K-nearest neighbors, support vector machines, kernelsupport vector machines, naive bayes, decision tree classification,random forest classification, K-means clustering, hierarchicalclustering, dimensionality reduction, principal component analysis,linear discriminant analysis, kernel principal component analysis,Q-learning, State Action Reward State Action (SARSA), Deep-Q network,Markov decision processes, Deep Deterministic Policy Gradient (DDPG), orthe like thereof. The recognition model may be trained as a function ofa recognition training set. As used in this disclosure “recognitiontraining set” is a training set that correlates at least a recognitionelement to at least an identification element. As a non-limitingexample, the recognition training set may relate a recognition elementof a facial pattern of an individual to an identification element of anidentification badge. As a further non-limiting example, the recognitiontraining set may relate a recognition element of a retinal scan to anidentification element of a security clearance.

Still referring to FIG. 30 , apparatus 3004 may receive the recognitionmodel from a remote device. As used in this disclosure a “remote device”is a computing system external to the apparatus that obtains and/orsends information relating to the recognition model. The remote devicemay provide modifications to the recognition model. For example, andwithout limitation, a modification may be comprised of a firmwareupdate, a software update, a recognition model correction, and the likethereof. As a non-limiting example a software update may incorporate anew recognition model that relates to a recognition element to amodified identification element. As a further non-limiting example aremote device may transmit a modified recognition model, wherein themodified recognition model may relate new identification elements topreviously identified recognition elements of a plurality of recognitionelements. Additionally or alternatively, the recognition model may betransmitted to the remote device, wherein the remote device may updatethe recognition training data and transmit an updated recognition modelback to apparatus 3004. The updated recognition model may be transmittedby the remote device and may be received by apparatus 3004 as a softwareupdate, firmware update, or corrected recognition machine-learningmodel. Additionally or alternatively, the remote device may include therecognition model, wherein apparatus 3004 transmits a signal, bit,datum, or parameter to the remote device and receives the outputtedidentified individual from the recognition model on the remote device.

Still referring to FIG. 30 , the recognition model may be generated as afunction of a classifier. A “classifier,” as used in this disclosure isa machine-learning model, such as a mathematical model, neural net, orprogram generated by a machine-learning algorithm known as a“classification algorithm,” that sorts inputs into categories or bins ofdata, outputting the categories or bins of data and/or labels associatedtherewith. A classifier may be configured to output at least a datumthat labels or otherwise identifies a set of data that are clusteredtogether, found to be close under a distance metric as described below,or the like. Apparatus 3004 and/or another device may generate aclassifier using a classification algorithm, defined as a processwhereby apparatus 3004 derives a classifier from training data.Classification may be performed using, without limitation, linearclassifiers such as without limitation logistic regression and/or naiveBayes classifiers, nearest neighbor classifiers such as k-nearestneighbors classifiers, support vector machines, least squares supportvector machines, fisher's linear discriminant, quadratic classifiers,decision trees, boosted trees, random forest classifiers, learningvector quantization, and/or neural network-based classifiers.

Still referring to FIG. 30 , apparatus 3004 may be configured togenerate a classifier using a Naïve Bayes classification algorithm.Naïve Bayes classification algorithm generates classifiers by assigningclass labels to problem instances, represented as vectors of elementvalues. Class labels are drawn from a finite set. Naïve Bayesclassification algorithm may include generating a family of algorithmsthat assume that the value of a particular element is independent of thevalue of any other element, given a class variable. Naïve Bayesclassification algorithm may be based on Bayes Theorem expressed asP(A/B)=P(B/A) P(A)÷P(B), where P(A/B) is the probability of hypothesis Agiven data B also known as posterior probability; P(B/A) is theprobability of data B given that the hypothesis A was true; P(A) is theprobability of hypothesis A being true regardless of data also known asprior probability of A; and P(B) is the probability of the dataregardless of the hypothesis. A naive Bayes algorithm may be generatedby first transforming training data into a frequency table. Apparatus3004 may then calculate a likelihood table by calculating probabilitiesof different data entries and classification labels. Apparatus 3004 mayutilize a naive Bayes equation to calculate a posterior probability foreach class. A class containing the highest posterior probability is theoutcome of prediction. Naïve Bayes classification algorithm may includea gaussian model that follows a normal distribution. Naïve Bayesclassification algorithm may include a multinomial model that is usedfor discrete counts. Naïve Bayes classification algorithm may include aBernoulli model that may be utilized when vectors are binary.

With continued reference to FIG. 30 , apparatus 3004 may be configuredto generate a classifier using a K-nearest neighbors (KNN) algorithm. A“K-nearest neighbors algorithm” as used in this disclosure, includes aclassification method that utilizes feature similarity to analyze howclosely out-of-sample-features resemble training data to classify inputdata to one or more clusters and/or categories of features asrepresented in training data; this may be performed by representing bothtraining data and input data in vector forms, and using one or moremeasures of vector similarity to identify classifications withintraining data, and to determine a classification of input data.K-nearest neighbors algorithm may include specifying a K-value, or anumber directing the classifier to select the k most similar entriestraining data to a given sample, determining the most common classifierof the entries in the database, and classifying the known sample; thismay be performed recursively and/or iteratively to generate a classifierthat may be used to classify input data as further samples. Forinstance, an initial set of samples may be performed to cover an initialheuristic and/or “first guess” at an output and/or relationship, whichmay be seeded, without limitation, using expert input received accordingto any process as described herein. As a non-limiting example, aninitial heuristic may include a ranking of associations between inputsand elements of training data. Heuristic may include selecting somenumber of highest-ranking associations and/or training data elements.

With continued reference to FIG. 30 , generating k-nearest neighborsalgorithm may generate a first vector output containing a data entrycluster, generating a second vector output containing an input data, andcalculate the distance between the first vector output and the secondvector output using any suitable norm such as cosine similarity,Euclidean distance measurement, or the like. Each vector output may berepresented, without limitation, as an n-tuple of values, where n is atleast two values. Each value of n-tuple of values may represent ameasurement or other quantitative value associated with a given categoryof data, or attribute, examples of which are provided in further detailbelow; a vector may be represented, without limitation, in n-dimensionalspace using an axis per category of value represented in n-tuple ofvalues, such that a vector has a geometric direction characterizing therelative quantities of attributes in the n-tuple as compared to eachother. Two vectors may be considered equivalent where their directions,and/or the relative quantities of values within each vector as comparedto each other, are the same; thus, as a non-limiting example, a vectorrepresented as [5, 10, 15] may be treated as equivalent, for purposes ofthis disclosure, as a vector represented as [1, 2, 3]. Vectors may bemore similar where their directions are more similar, and more differentwhere their directions are more divergent; however, vector similaritymay alternatively or additionally be determined using averages ofsimilarities between like attributes, or any other measure of similaritysuitable for any n-tuple of values, or aggregation of numericalsimilarity measures for the purposes of loss functions as described infurther detail below. Any vectors as described herein may be scaled,such that each vector represents each attribute along an equivalentscale of values. Each vector may be “normalized,” or divided by a“length” attribute, such as a length attribute l as derived using aPythagorean norm: l=Σ_(i=0) ^(n)a_(i) ², where a, is attribute number iof the vector. Scaling and/or normalization may function to make vectorcomparison independent of absolute quantities of attributes, whilepreserving any dependency on similarity of attributes; this may, forinstance, be advantageous where cases represented in training data arerepresented by different quantities of samples, which may result inproportionally equivalent vectors with divergent values.

Still referring to FIG. 30 , apparatus 3004 identifies a behavior 3012of a first individual of a plurality of individuals as a function ofsensor data 3008. As used in this disclosure a “behavior” is an actionand mannerism made by an individual, organism, system, or artificialentities in conjunction with themselves or their environment, whichincludes the other systems or organisms around as well as the physicalenvironment. Behavior 3012 may include, without limitation, overtbehavior, wherein overt behavior is a visible type of behavior that canoccur outside of a human being. Overt behavior may include, withoutlimitation, eating food, riding a bicycle, playing football, walking ina secure area, or the like thereof. Behavior 3012 may include, withoutlimitation, covert behavior, wherein covert behavior is not visible toanother individual. Covert behavior may include, without limitation,thoughts, emotions, feelings, or the like thereof. Behavior 3012 mayinclude, without limitation, molecular behavior, wherein molecularbehavior includes unexpected behavior that occurs without thinking,which can be broken down into atomistic parts or molecules. Molecularbehavior may include, without limitation, an individual that closestheir eyes when something is about to interact with that individual'seyes. Behavior 3012 may include, without limitation, molar behavior,wherein molar behavior is a behavior that is identified in terms of theultimate cause of history. Molar Behavior may include, withoutlimitation, a person that loves someone is merely exhibiting a patternof loving behavior over time, as love would be considered atomistic andmust be looked in more wholistic terms. Behavior 3012 may include,without limitation, voluntary behavior, wherein voluntary behavior is atype of behavior that depends on a human want, desire, wish, yearning,or the like thereof. Voluntary behavior may include, without limitation,walking, speaking, writing, striking, and the like thereof. Behavior3012 may include, without limitation, involuntary behavior, whereininvoluntary behavior is a behavior that naturally occurs withoutthinking. Voluntary behavior may include, without limitation, breathing,blinking, swallowing, digestion, or the like thereof. Behavior 3012 mayinclude behavior that is considered to be positive, negative, and/orneutral behavior. As used in this disclosure “positive behavior” isbehavior that is perceived by another individual, organism, artificialintelligence, or entity as a good act. As a non-limiting examplepositive behavior may include altruistic behavior, caring behavior,compassionate behavior, considerate behavior, faithful behavior,impartial behavior, kind behavior, pleasant behavior, polite behavior,sincere behavior, and the like thereof. As used in this closure a“negative behavior” is behavior that is perceived by another individual,organism, artificial intelligence, or entity as a bad act. As anon-limiting example, a negative behavior may include aggressivebehavior, argumentative behavior, bossy behavior, deceitful behavior,domineering behavior, flaky behavior, inconsiderate behavior,manipulative behavior, rude behavior, spiteful behavior, and the likethereof. As used in this disclosure “neutral behavior” is behavior thatis perceived by another individual, organism, artificial intelligence,or entity as a behavior that does not attempt to display any positive ornegative intentions. As a non-limiting example, a neutral behavior mayinclude apathetic behavior, indifferent behavior, behavior indicative ofa lack of conviction, or the like.

Still referring to FIG. 30 , As used in this disclosure a “firstindividual” is first person that is identified among a crowd ofindividuals. For example and without limitation, a single person may beidentified out of a crowd of 50 people. Apparatus 3004 may identify afirst individual as a function of identifying a crowd. As used in thisdisclosure a “crowd” is a group of people gathered together in adisorganized and/or organized manner. For example, and withoutlimitation a crowd may include a large group of individuals thatsurround a government facility, gather in a public street, or the like.As a further non-limiting example, a crowd may include a small group ofindividuals that penetrate a government building. As a furthernon-limiting example, a crowd may include 5 individuals that enter asecured area. As a further non-limiting example a crowd may include agathering of more than 100,000 protestors in an area. A crowd may beclassified according to a degree of definiteness and constancy ofconsciousness of a group of people comprising the crowd. For example,and without limitation a crowd containing people having a very similarset of goals or performing a very similar set of actions may beclassified as a homogeneous crowd. As a further non-limiting example, acrowd containing people who are seeking goals unrelated to one anotherand/or engaging in two or more unrelated activities may be classified asa heterogeneous crowd. Crowds may include a multitude of individuals andsmall groups that have temporarily assembled. As used in this disclosurea “small group” is one or more individuals that share similar thoughtsand/or beliefs for entering the crowd. For example, and withoutlimitation small groups may include friends, family members, and/oracquaintances. Apparatus may use retroreflection to count people in aconcert and/or crowd. An apparatus may use retroreflection to detectlocation of persons in a space for entertainment purposes, such as lightshows.

In an embodiment and still referring to FIG. 30 , small groups mayconsist of one or more influencers. As used in this disclosure a“influencer” is one or more individuals that work tougher to achieve agreater result than they would individually. Influencers may include,without limitation, one or more orators, musicians, athletes, and/orsome other individual who moves a crowd to a point of agreement beforemaking a specific call to action. Small groups may include one or moresupporters. For example, and without limitation a crowd influencer mayinclude an individual speaking at a seminar. As a further non-limitingexample, an influencer may include one or more political figures and/orpersons of influence. Additionally or alternatively, an influencer maypossess a prestigiousness, wherein a “prestigiousness”, as describedhere in, is a domination exercised on our mind by an individual, a work,or an idea. For example, and without limitation an individual maypossess a prestigiousness as a function of a job title, uniform, judge'srobe, mental acuity, physical strength, and the like thereof. In anembodiment, and without limitation, small groups may include one or moresupporters. As used in this disclosure a “supporter” is an individualthat agrees and/or believes the influencer's call to action. Forexample, a support may perform an action and/or behavior as a functionof an influencer's verbal statements. As a further non-limiting example,a supporter may include an individual that attempts to encourage otherindividuals to actively listen to and/or believe the influencersstatements.

Still referring to FIG. 30 , crowds may include any number ofindividuals that perform an assembling process. As used in thisdisclosure an “assembling process” is a process wherein individualstemporarily assembly for a specific amount of time, wherein a specificamount of time may include seconds, minutes, hours, days, weeks, months,and the like thereof. Crowds may perform an assembling process as afunction of an organized mobilization method and/or an impromptuprocess, such as word of mouth by a support and/or third party. In anembodiment and without limitation, crowds may include any number ofindividuals that perform a temporary gathering. As used in thisdisclosure a “temporary gathering” is a gathering and/or assemblage ofindividuals to participate in both individual and/or collective actions.For example, and without limitation crowds may perform a temporarygathering by participating in the activities of the crowd as acollective group. As a further non-limiting example a temporarygathering may include individuals that gather to riot, loot, deface,and/or perform some other activity collectively. In another embodiment,and without limitation, crowds may include performing a dispersingprocess. As used in this disclosure a “dispersing process” is a processwherein the crowd's individuals disperse from the location of thegathering. As a non-limiting example, a crowd may perform a dispersingprocess by leaving a first location and traveling to an alternatelocation. As a further non-limiting example, a crowd may perform adispersing process by leaving a first location and dividing into smallercrowd's to one or more subsequent locations.

Still referring to FIG. 30 , crowds may be determined according to oneor more small group classifiers. A “small group classifier,” as used inthis disclosure is a machine-learning model, such as a mathematicalmodel, neural net, or program generated by a machine learning algorithmknown as a “classification algorithm,” as described in further detailabove, that sorts inputs into categories or bins of data, outputting thecategories or bins of data and/or labels associated therewith. A smallgroup classifier may include any of the classifier as described indetail above. A small classifier may be configured to output at least adatum that labels or otherwise identifies a set of data that areclustered together, found to be close under a distance metric asdescribed below, or the like. Apparatus 3004 and/or another device maygenerate a classifier using a classification algorithm, defined as aprocesses whereby an apparatus 3004 derives a classifier from trainingdata. Classification may be performed using, without limitation, linearclassifiers such as without limitation logistic regression and/or naiveBayes classifiers, nearest neighbor classifiers such as k-nearestneighbors classifiers, support vector machines, least squares supportvector machines, fisher's linear discriminant, quadratic classifiers,decision trees, boosted trees, random forest classifiers, learningvector quantization, and/or neural network-based classifiers.

Still referring to FIG. 30 , apparatus 3004 may identify behavior 3012of the first individual by identifying at least a physiological actionas a function of sensor data 3008. As used in this disclosure a“physiological action” is a physical, psychological, and/or spiritualdecision that is acted upon, wherein that action at least impacts one ormore surrounding individuals. For example, and without limitation aphysiological action may include striking an individual, walking into asecure area, coughing on an individual, assaulting an individual,verbally abusing an individual, verbally discriminating against anotherindividuals religious beliefs, sitting on a chair, complimenting anindividual, opening a door for an individual and the like thereof.

Still referring to FIG. 30 , apparatus 3004 may identify behavior 3012using the physiological action, an at least accepted standard, and abehavior model. As used in this disclosure an “accepted standard” is oneor more ethical constructs that are established by society to promotetrust, fairness and or kindness among a society. An accepted standardmay include, without limitation, a utilitarian approach, wherein theutilitarian approach may include actions that are assessed in terms ofits consequences and/or outcomes and strives. As a non-limiting examplean individual that is displaying behavior for a selfish, wherein theoutcome is personal gain may be identified as a negative acceptedstandard. An accepted standard may include, without limitation, a rightsapproach, wherein the rights approach may include actions that bestprotect and respect the moral rights of individuals. As a non-limitingexample moral rights may include, without limitation, rights to makeone's own choices, to be told the truth, not to be injured, a degree ofprivacy, and the like thereof. An accepted standard may include, withoutlimitation, a justice approach, the justice approach may include actionsthat relate to equality and/or justice. As a non-limiting exampleactions such as discrimination and/or favoritism may not be consideredequality behaviors, whilst actions of impartiality and/or consideratemay be considered justice behaviors. An accepted standard may include,without limitation, a common good approach; common good approach mayinclude actions that are taken in order to benefit not only a certaingroup of individuals, but the society as a whole. As a non-limitingexample actions of an individual tampering with a building may denote anegative behavior, while actions of cleaning a public hallway orremoving litter from a secure area may consist of a positive behavior.An accepted standard may include, without limitation, a virtue approach;the virtue approach may include actions that are taken in order toachieve a full development of our humanity. As a non-limiting exampleactions relating to honesty, courage, compassion, generosity, tolerance,love, fidelity, integrity, fairness, self-control, and prudence may allbe considered virtues that may aid in achieving full development of anindividual's humanity.

As used in this disclosure, and with further reference to FIG. 30 , a“behavior model” is a machine-learning model to produce outputs ofbehaviors given sensor data provided as inputs; this is in contrast to anon-machine learning software program where the commands to be executedare determined in advance by a user and written in a programminglanguage. A behavior model may include any supervised, unsupervised, orreinforcement machine-learning process that apparatus 3004 and/or aremote server may or may not use in the determination of the behavior. Abehavior model may include, without limitation machine learningprocesses such as simple linear regression, multiple linear regression,polynomial regression, support vector regression, ridge regression,lasso regression, elasticnet regression, decision tree regression,random forest regression, logistic regression, logistic classification,K-nearest neighbors, support vector machines, kernel support vectormachines, naive bayes, decision tree classification, random forestclassification, K-means clustering, hierarchical clustering,dimensionality reduction, principal component analysis, lineardiscriminant analysis, kernel principal component analysis, Q-learning,State Action Reward State Action (SARSA), Deep-Q network, Markovdecision processes, Deep Deterministic Policy Gradient (DDPG), or thelike thereof.

Still referring to FIG. 30 , apparatus 3004 may generate behavior modelusing behavior training data. As used in this disclosure “behaviortraining data” is training data that correlates at least a physiologicalaction and/or accepted standards from the plurality of sensors to atleast a behavior, wherein a sensor is a device that detects and/ormeasures a physical property of an external surrounding, as describedabove in detail. For example, and without limitation a physiologicalaction of assault may relate to an accepted standard of a negativebehavior under the ethical construct of a justice approach. The behaviortraining data may be received as a function of user-entered valuationsof behavior. The behavior training data may be received by one or morepast iterations of the previous behavior identifications. Behaviortraining data may be received by one or more remote devices that atleast correlate a physiological action and/or accepted standard to abehavior.

Further referring to FIG. 30 , apparatus 3004 may alternatively oradditionally receive the behavior model from a remote device, wherein aremote device includes a secondary external computing device and orapparatus, as discussed in detail above. The remote device may providemodifications to the behavior model. For example, and withoutlimitation, a modification may be comprised of a firmware update, asoftware update, a behavior model correction, and the like thereof. As anon-limiting example a software update may incorporate a new behaviormodel that relates to a physiological action to a modified acceptedstandard. As a further non-limiting example a remote device may transmita modified behavior model, wherein the modified behavior model mayrelate new accepted standards to previously identified physiologicalactions of a plurality of physiological actions. Additionally oralternatively, the behavior model may be transmitted to the remotedevice, wherein the remote device may update the behavior training dataand transmit an updated behavior model back to apparatus 3004. Theupdated behavior model may be transmitted by the remote device and maybe received by apparatus 3004 as a software update, firmware update, orcorrected behavior machine-learning model. Additionally oralternatively, the remote device may include the behavior model, whereinapparatus 3004 transmits a signal, bit, datum, or parameter to theremote device and receives the outputted behavior from the behaviormodel on the remote device.

In an embodiment and still referring to FIG. 30 , apparatus 3004 mayidentify a behavior 3012 as a function of crowds and/or small groups.For example, and without limitation, a physiological action of looting,rioting, and/or damaging federal property may be determined as afunction of a crowd and/or small group. As a further non-limitingexample, apparatus 3004 may identify a behavior associated with asubmergence. As used in this disclosure a “submergence” is adisappearance of a conscious personality and the appearance of anunconscious personality. For example, and without limitation a behaviorof submergence may include behaviors such as increased violence and/oraggression as a function of a mental unity in a riot. As a furthernon-limiting example, apparatus 3004 may identify a behavior associatedwith a contagion. As used in this disclosure a “contagion” is an actthat is contagious and/or transcends to multiple individuals. Forexample, and without limitation a contagion behavior may include abehavior of rioting as a function of sacrificing personal interest of aprotest towards a collective interest of harming other individualsand/or enhancing the riots message. As a further non-limiting example,apparatus 3004 may identify a behavior associated with a suggestibilityas a result of a hypnotic state. As used in this disclosure a“suggestibility as a result of a hypnotic state” is an act that is bentand/or controlled towards the direction determined by the influencer.For example, and without limitation a suggestibility as a result of ahypnotic state behavior may include a behavior of performing actionsand/or behaviors that the crowd is performing without thinking as anindividual.

In an embodiment, and still referring to FIG. 30 , apparatus 3004 mayidentify a behavior 3012 of crowds as a function of a crowd behavior. Asused in this disclosure a “crowd behavior” is a behavior and/or actionconducted by a crowd. Crowd behavior may include a casual crowd, whereina “casual behavior”, as described herein, is a behavior of a collectionof individuals who happen to be in the same place at the same time.Casual crowds may have no real common bond, long-term purpose, oridentity. Crowd behavior may include a conventional behavior. As used inthis disclosure a “conventional behavior” is a behavior of a collectionof individuals who gather for a purpose. For example, and withoutlimitation a conventional behavior may include an attending a movie, aplay, a concert, and/or a lecture. Crowd behavior may include anexpressive behavior. As used in this disclosure an “expressive behavior”is a behavior of a collection of individuals that gather primarily to beexcited and to express one or more emotions. Expressive behavior mayinclude, without limitation, one or more behaviors such as religiousrevivals, political rallies, holiday events, such as Mardi Gras, and thelike thereof. Crowd behavior may include an acting behavior. As used inthis disclosure an “acting behavior” is a behavior of a collection ofindividuals that display violent and/or destructive actions. Forexample, acting behavior may include looting, and/or behaviorsassociated with violence such as property damage, theft, trespassing,and the like thereof. For example and without limitation, apparatus 3004may identify a small group exhibiting acting behavior in a crowd such asattempting to overturn a car and/or break car windows. As a furthernon-limiting example, apparatus 3004 may identify a crowd exhibitingexpressive behavior such as displaying support signs for a politicalleader. As a further non-limiting example, apparatus 3004 may identifyan expressive crowd for a political rally, wherein apparatus 3004identifies a small group within the crowd that is exhibiting actingbehavior by throwing rocks and/or storming a government building.

Still referring to FIG. 30 , apparatus 3004 is configured to determineas a function of the individual behavior 3012 at least a behaviorelement 3016. As used in this disclosure a “behavioral element” isinformation that at least relates to an individual's actions as afunction of the individual's behavior. As a non-limiting example, abehavioral element may include language, tone, word selection,physiological actions, and the like thereof. For example, and withoutlimitation, a behavioral element of assault may be identified as afunction a behavior of aggression. As a further non-limiting example abehavioral element of demeaning tone may be identified from a behaviorof frustrated. As a further non-limiting example a behavior element mayinclude a microexpression that at least relates to a behavior, such aswhen an individual has an anxious microexpression for a behavior oftrespassing. Apparatus 3004 may determine behavior element 3016 byreceiving an assemblage comparator. As used in this disclosure an“assemblage comparator” is a measurable value that at least relatessurrounding individual's behaviors to the first individual's behavior.For example, and without limitation, an assemblage comparator may be avalue of 90 for a first individual that presents a behavior ofaggression, wherein the surrounding individual's behaviors indicate apositive behavior such as kindness. As a further non-limiting example,an assemblage comparator may be a value of 20 for a first individual'sbehavior of aggression, wherein the surrounding individual's behaviorsare also aggression. Behavior element 3016 may be determined as afunction of the assemblage comparator and an assemblage model. As usedin this disclosure an “assemblage model” is a machine-learning model toproduce outputs of behavioral elements given an individual's behaviorprovided as inputs; this is in contrast to a non-machine learningsoftware program where the commands to be executed are determined inadvance by a user and written in a programming language. An assemblagemodel may include any supervised, unsupervised, or reinforcementmachine-learning process that apparatus 3004 and/or a remote server mayor may not use in the determination of the behavior element. Anassemblage model may include, without limitation, machine learningprocesses such as simple linear regression, multiple linear regression,polynomial regression, support vector regression, ridge regression,lasso regression, elasticnet regression, decision tree regression,random forest regression, logistic regression, logistic classification,K-nearest neighbors, support vector machines, kernel support vectormachines, naive bayes, decision tree classification, random forestclassification, K-means clustering, hierarchical clustering,dimensionality reduction, principal component analysis, lineardiscriminant analysis, kernel principal component analysis, Q-learning,State Action Reward State Action (SARSA), Deep-Q network, Markovdecision processes, Deep Deterministic Policy Gradient (DDPG), or thelike thereof. An assemblage model may be trained as a function of anassemblage training set.

Still referring to FIG. 30 , apparatus 3004 may generate assemblagemodel using assemblage training data. As used in this disclosure“assemblage training data” is training data that correlates at least arelative behavior of an individual and a behavioral element, wherein arelative behavior is an individual's behavior in relation to theassemblage's behavior. For example, and without limitation a relativebehavior may include a relative behavior of dishonesty, wherein thebehavioral element may include trespassing. As a further non-limitingexample, a relative behavior of theft may correlate to a behavioralelement of illegal action. The assemblage training data may be receivedas a function of user-entered valuations of behavioral elements. Theassemblage training data may be received by one or more past iterationsof the previous behavior elements correlating to relative behaviors.Assemblage training data may be received by one or more remote devicesthat at least correlate relative behavior to a behavioral element.

Still referring to FIG. 30 , apparatus 3004 may receive the assemblagemodel from a remote device, wherein a remote device includes a secondaryexternal computing device and or apparatus, as discussed in detailabove. The remote device may provide modifications to the assemblagemodel. For example, and without limitation, a modification may becomprised of a firmware update, a software update, an assemblage modelcorrection, and the like thereof. As a non-limiting example a softwareupdate may incorporate a new assemblage model that relates an assemblagebehavior to a modified individual behavior. As a further non-limitingexample a remote device may transmit a modified assemblage model,wherein the modified assemblage model may relate new individualbehaviors to previously identified assemblage behaviors. Additionally oralternatively, the assemblage model may be transmitted to the remotedevice, wherein the remote device may update the assemblage trainingdata and transmit an updated assemblage model and/or updated assemblagetraining data back to apparatus 3004. The updated assemblage model maybe transmitted by the remote device and may be received by apparatus3004 as a software update, firmware update, corrected assemblagetraining data, and/or corrected assemblage machine-learning model. Theupdated assemblage training data may change and/or modify the weightsand/or coefficients, which a hardware implementation may obtain as aresult of an online database. Additionally or alternatively, the remotedevice may include the assemblage model, wherein apparatus 3004transmits a signal, bit, datum, or parameter to the remote device andreceives the outputted behavior element from the assemblage model on theremote device.

Still referring to FIG. 30 , apparatus 3004 is configured to generate abehavioral remedy 3020 that alters at least behavioral element 3016. Asused in this disclosure a “behavioral remedy” is a variable associatedwith at least altering and/or changing an individual behavior.Behavioral remedy 3020 may include at least an external stimulus and/oroutput that may interact with an individual, such that a behavior isaltered. For example, and without limitation a behavioral remedy mayinclude a loud noise that at least stops an individual from conductingthe identified behavior. Behavioral remedy 3020 is generated byidentifying at least a deterrent 3024 of a plurality of deterrents. Asused in this disclosure a “deterrent” is a thing, entity, object, and/oraction that intends to discourage and/or prevent an individual fromcontinuing an action, behavior, and/or conduct. Deterrent 3024 mayinclude without limitation, directed light, sounds, electricaldeterrents, neurostimulators, chemicals, entanglement devices, and thelike thereof. As used in this disclosure a “directed light deterrent” isa deterrent that uses a high-intensity light source such as, but notlimited to, a laser, super LED, laser illuminated LED, super-luminescentLED, VCSEL, plasma discharge lamp, and/or high-intensity LED that isactively aimed at and/or focused on an individual, to generate adeterrent effect. As used in this disclosure a “directed sounddeterrent” is a sound source that is aimed at a specific individual in amanner analogous to a directed light source. A directed sound deterrentmay include, without limitation a long-range acoustic device (LRAD), alaser generating localized plasmas in the atmosphere to createmodulating plasmas near the individual such that audible sound isproduced, an ultrasonic carrier wave, and the like thereof. As used inthis disclosure “neurostimulation deterrents” is an electrical sourcethat is projected at an individual such that an electrical contact isachieved between an individual and the deterrent. As a non-limitingexample, an electrical shock deterrent may include a HumanElectro-Muscular Incapacitation (HEMI) device, a stun gun, a taser,Taser Area Denial System (TADS), a plasma, an electric field, anionizer, and the like thereof. As used in this disclosure a “chemicaldeterrent” is a chemical and/or molecule that at least provide a noxiousand/or discomforting experience for an individual. For example, andwithout limitation, a chemical deterrent may include pepper spray,malodorant weapons, tear gas, pacifying agent, white phosphorous,aerosolized opioids and the like. As used in this disclosure“entanglement devices” are deterrents wherein an individual becomesphysically trapped in a device and prevents escape of that individual.For example, an entanglement device may include, without limitationnets, bolas, and/or other entanglement or entrapment devices that arelaunched ballistically at the individual in order to limit or stop theindividual's ability to move normally. Behavioral remedy may includespray-painting noise over crowd to prevent communication. Noise, such aswhite noise, that exceeds the 95 DB decibel level of scream by 25 dB125-130 dB may effectively prevent communication between people in thecrowd.

Still referring to FIG. 30 , apparatus 3004 generates behavioral remedy3020 by determining a collateral parameter 3028 related to thedeterrent. As used in this disclosure a “collateral parameter” is avalue that at least relates to the amount of death, injury, and/ordamage inflicted to an assemblage that is an incidental result of adeterrent being applied to a first individual. For example and withoutlimitation, collateral parameter 3028 may indicate a value of 50 for adeterrent of an audio output that is administered as a result of anindividual in an assemblage that is trespassing in a secure area. As afurther non-limiting example collateral parameter 3028 may have a valueof 10 for a deterrent of a directed laser output directed towards anindividual in an assemblage exhibiting a behavior of theft. As a furthernon-limiting example collateral parameter 3028 may include a value of3000 for a chemical deterrent that is administered due to an individualbehavior of assault. Apparatus 3004 generates behavioral remedy 3020 asa function of collateral parameter 3028 and a congestion variable 3032.As used in this disclosure a “congestion variable” is a parameter thatrelates to the concentration of the assemblage with respect to thenumber of individuals in a given area. Congestion variable 3032 mayinclude the number of individuals in a given area, wherein the arerelated to a specific volume of space. As a non-limiting examplecongestion variable 3032 may indicate that a total of 10 individuals arein a secure area of 50 ft and/or 4.65 m². As a further non-limitingexample, congestion variable 3032 may indicate that a total of 15individuals are in an area of 10 ft×22 ft×10 ft and/or 3.048 m×6.7056m×3.048 m. Congestion variable 3032 may indicate a maximum number ofindividuals that may enter a specific area. For example, and withoutlimitation congestion variable 3032 may indicate that an assemblage isnever to exceed a maximum of 23 individuals in a secure area that is 32ft and/or 2.97 m². As a further non-limiting example, congestionvariable 3032 may indicate that an assemblage exceeds the maximum numberof 45 individuals in an area of 150 ft and/or 13.94 m².

Still referring to FIG. 30 , apparatus 3004 may generate congestionvariable 3032 by receiving at least a spatiotemporal element of anindividual of a plurality of individuals in the assemblage. As used inthis disclosure a “spatiotemporal element” is datum relating to bothspace and time of an individual's physical being. As a non-limitingexample a spatiotemporal element may include a movement of an individualwalking within an assemblage. As a further non-limiting example, aspatiotemporal element may identify a location of an individual withinan assemblage. Additionally or alternatively, apparatus 3004 mayidentify a social distance metric as a function of the spatiotemporalelement, which relates to the at least an individual. As used in thisdisclosure a “social distance metric” is the total distance that existsbetween individuals, wherein distance denotes a measurable value of alength between two individuals including, but not limited to a thou,line, inch, foot, yard, mile, league, fathom, nautical mile, millimeter,centimeter, meter, decameter, hectometer, and the like thereof. As anon-limiting example a social distance metric may include identifying adistance of 6 feet between individuals in an assemblage. As a furthernon-limiting example a social distance metric may include identifying adistance of 10 meters between individuals in an assemblage. As a furthernon-limiting example, a social distance metric may include identifying adistance of 10 centimeters between individuals in an assemblage.Apparatus 3004 may generate congestion variable 3032 as a function ofthe social distance metric.

In an embodiment and still referring to FIG. 30 , apparatus 3004 maygenerate a behavioral remedy as a function of an individual, crowd,and/or small group. As a non-limiting example a behavioral remedy maygenerate a directed light deterrent, such as a laser, for an individualthat is displaying aggressive and/or violent behaviors. As a furthernon-limiting example, a behavioral remedy may generate a directed soundsource as a function of a crowd, such as a loud and/or incapacitatingnoise to at least mitigate and/or prevent the crowd's negativebehaviors. As a further non-limiting example, apparatus 3004 maygenerate a behavioral remedy of a chemical deterrent applied to a smallgroup of influencers to at least prevent the assembly and/or temporarygathering process. As a further non-limiting example, a directed sounddeterrent may be generated as a function of a small group of supportersthat are attempting to gather and/or coordinate external individuals tojoin the crowd, wherein the directed sound deterrent may only affect thesmall group of supporters.

Still referring to FIG. 30 , apparatus 3004 administers deterrent as afunction of behavioral remedy 3020 that alters behavior 3012 of thefirst individual. Apparatus 3004 may administer deterrent by identifyingthe at least a deterrent as a function of the plurality of deterrents.Apparatus 3004 may administer deterrent 3020 by identifying a storedprevious deterrent action according to previously generated behavioralremedies. Apparatus 3004 may administer deterrent 3020 using a linearprogramming algorithm. A linear programming algorithm may be used toidentify a deterrent as a function of the behavioral remedy and thebehavior of the first individual and determine the deterrent that atleast minimizes a distance metric, wherein apparatus 3004 may use alinear program such as, without limitation, a mixed-integer program. A“linear program,” as used in this disclosure, is a program thatoptimizes a linear objective function, given at least a constraint. As anon-limiting example, apparatus 3004 may calculate variables of set ofbehavioral remedies of such parameters from goal parameters, includingbut not limited to altering the behavior of the first individual,reducing a collateral parameter, reducing a congestion variable, and/oridentifying a subsequent deterrent; calculate an output of a deterrentusing the variables; and select the deterrent having the largest size,according to a given definition of “size,” of the set of deterrentoutputs representing the selected deterrents; size may, for instance,include absolute value, numerical size, or the like. Additionally oralternatively, apparatus 3004 may administer deterrent 3020 as afunction of one or more scoring processes, conic programming algorithms,geometric programming algorithms, integer programming algorithms,fractional programming algorithms, non-linear programing algorithms,and/or mathematical optimization programming algorithms.

Now referring to FIG. 31 , an exemplary embodiment of a system 3100 foridentifying a behavior is illustrated. Apparatus 3004 may identifybehavior 3012 of the first individual by identifying at least aphysiological action 3104 as a function of sensor data 3008, whereinphysiological data is described in detail above. Physiological action3104 may include striking an individual, entering an assemblage,coughing in an assemblage, verbally abusing an individual in anassemblage, discriminating against another individuals religiousbeliefs, sitting on a chair, complimenting an individual, opening a doorfor an individual and the like thereof. Behavior 3012 may includereceiving at least an accepted standard 3108, wherein an acceptedstandard is one or more ethical constructs that are established bysociety to promote trust, fairness and or kindness among a society asdiscussed above in detail. An accepted standard may be generated fromone or more ethical constructs 3112. As used in this disclosure an“ethical construct” is one or more beliefs and/or principles held by aperson or group about how to determine which human interactions areright or wrong. Ethical constructs 3112 may include a utilitarianapproach, a rights approach, a justice approach, a common good approach,and or a virtue approach, as described in detail above. As anon-limiting example a utilitarian approach may include an individualthat is displaying behavior for a selfish, wherein the outcome ispersonal gain may be identified as a negative accepted standard. As anon-limiting example a rights approach may include, without limitation,rights to make one's own choices, to be told the truth, not to beinjured, a degree of privacy, and the like thereof. As a non-limitingexample a justice approach may include actions such as discriminationand/or favoritism may not be considered equality behaviors, whilstactions of impartiality and/or considerate may be considered justicebehaviors. As a non-limiting example a common good approach may includeactions of an individual tampering with a building may denote a negativebehavior, while actions of cleaning a public hallway or removing litterfrom a secure area may consist of a positive behavior. As a non-limitingexample, a virtue approach may include actions relating to honesty,courage, compassion, generosity, tolerance, love, fidelity, integrity,fairness, self-control, and prudence which may all be considered virtuesthat may aid in achieving full development of an individual's humanity.Behavior 3012 may include receiving at least a behavior model 3116,wherein a behavior model is a machine-learning process that usestraining data and/or training set to generate an algorithm that will beperformed by an apparatus and/or one or more remote servers to produceoutputs given data provided as inputs; this is in contrast to anon-machine learning software program where the commands to be executedare determined in advance by a user and written in a programminglanguage, as described above in detail. As a non-limiting example,behavior model 3116 may include any supervised, unsupervised, orreinforcement machine-learning process that apparatus 3004 and/or aremote server may or may not use in the determination of the behavior.Behavior model 3116 may include, without limitation machine learningprocesses such as simple linear regression, multiple linear regression,polynomial regression, support vector regression, ridge regression,lasso regression, elasticnet regression, decision tree regression,random forest regression, logistic regression, logistic classification,K-nearest neighbors, support vector machines, kernel support vectormachines, naive bayes, decision tree classification, random forestclassification, K-means clustering, hierarchical clustering,dimensionality reduction, principal component analysis, lineardiscriminant analysis, kernel principal component analysis, Q-learning,State Action Reward State Action (SARSA), Deep-Q network, Markovdecision processes, Deep Deterministic Policy Gradient (DDPG), or thelike thereof. Behavior model 3116 may be trained as a function of abehavior training set, wherein a behavior training set is a training setthat correlates at least a physiological action from the plurality ofsensors to at least an accepted standard, as described above in detail.Apparatus 3004 may receive behavior model 3116 from a remote device,wherein a remote device includes a secondary external computing deviceand or apparatus, as discussed in detail above. The remote device mayprovide modifications to behavior model 3116 through process of afirmware update, a software update, a behavior model correction, and thelike thereof. As a non-limiting example a software update mayincorporate a new behavior model that relates to a physiological actionto a modified accepted standard. Additionally or alternatively, behaviormodel 3116 may be transmitted to the remote device, wherein the remotedevice may update the behavior training data and transmit an updatedbehavior model back to apparatus 3004. The updated behavior model may betransmitted by the remote device and may be received by apparatus 3004as a software update, firmware update, or corrected behaviormachine-learning model. Additionally or alternatively, the remote devicemay include behavior model 3116, wherein apparatus 3004 transmits asignal, bit, datum, or parameter to the remote device and receives theoutputted behavior from behavior model 3116 on the remote device.

Now referring to FIG. 32 , an exemplary embodiment of an identificationdatabase 3204 is illustrated. Identification database 3204 may beimplemented, without limitation, as a relational database, a key-valueretrieval database such as a NOSQL database, or any other format orstructure for use as a database that a person skilled in the art wouldrecognize as suitable upon review of the entirety of this disclosure.Identification database 3204 may alternatively or additionally beimplemented using a distributed data storage protocol and/or datastructure, such as a distributed hash table or the like. Identificationdatabase 3204 may include a plurality of data entries and/or records asdescribed above. Data entries in a database may be flagged with orlinked to one or more additional elements of information, which may bereflected in data entry cells and/or in linked tables such as tablesrelated by one or more indices in a relational database. Persons skilledin the art, upon reviewing the entirety of this disclosure, will beaware of various ways in which data entries in a database may store,retrieve, organize, and/or reflect data and/or records as used herein,as well as categories and/or populations of data consistently with thisdisclosure. Identification database 3204 may include one or more tables,including without limitation, a federal tableset 3208; federal tableset3208 may include identification that relates an individual to a federaldatastore. As a non-limiting example federal tableset 3208 may include,without limitation, a national ID card, social security card, mailisolation control, integrated automated fingerprint identificationsystem, NSA call database, TALON, homeless management informationsystems and the like thereof. Identification database 3204 may includeone or more tables, including without limitation, a state tableset 3212;federal tableset 3212 may include identification that relates anindividual to a state datastore. As a non-limiting example statetableset 3212 may include, without limitation, state ID card, statedriver's license, state voter information, state hunting license,library card, marriage certificate, baptismal certificate, schoolrecord, automobile insurance, and the like thereof. Identificationdatabase 3204 may include one or more tables, including withoutlimitation, an employer tableset 3216; employer tableset 3216 mayinclude identification that relates an individual to an employerdatastore. As a non-limiting example employer tableset 3216 may include,without limitation, a W-2 wage and tax statement, a security clearanceidentification, salary identifiers, work permits, medical information,and the like thereof.

Now referring to FIG. 33 , an exemplary embodiment of a method 3300 ofaltering an individual behavior is illustrated. At step 3305, anapparatus 3004 receives a plurality of sensor data 3008. Sensor data3008 may be implemented, without limitation, as described above inreference to FIGS. 1-4 .

Still referring to FIG. 33 , at step 3310, apparatus 3004 identifies abehavior of a first individual of a plurality of individuals as afunction of the plurality of sensor data. Behavior 3012 may beimplemented, without limitation, as described above in reference toFIGS. 1-4 . An individual may be implemented, as described above inreference to FIGS. 1-4 . Behavior 3012 is identified as a function ofone or more physiological actions 3104, accepted standards 3108, and/orbehavior models 3116. Physiological action 3104 may be implemented,without limitation, as described above in reference to FIGS. 1-4 .Accepted standards 3108 may be implemented, without limitation, asdescribed above in reference to FIGS. 1-4 . Accepted standards 3108 mayinclude ethical constructs 3112. Ethical constructs 3112 may beimplemented, without limitation, as described above in reference toFIGS. 1-4 . Behavior 3012 may be identified by behavior model 3116.Behavior model 3116 may be implemented, without limitation, as describedabove in reference to FIGS. 1-4 . Behavior model 3116 may be configuredusing a behavior training data. The behavior training data may beimplemented, without limitation, as described above in reference toFIGS. 1-4 .

Still referring to FIG. 33 , at step 3315, apparatus 3004 determines asa function of the individual behavior at least a behavior element 3016.Behavior element 3016 may be implemented, without limitation, asdescribed above in reference to FIGS. 1-4 . Behavior element 3016 may beidentified from an assemblage comparator, wherein an assemblagecomparator may be implemented, without limitation, as described above inreference to FIGS. 1-4 . Behavior element 3016 may be determined as afunction of an assemblage model, wherein an assemblage model may beimplemented, without limitation, as described above in reference toFIGS. 1-4 . The assemblage model may be configured using an assemblagetraining data. The assemblage training data may be implemented, withoutlimitation, as described above in reference to FIGS. 1-4 .

Still referring to FIG. 33 , at step 3320, apparatus 3004 generates abehavior remedy 3020 that alters behavior element 3016. Behavior remedy3020 may be implemented, without limitation, as described above inreference to FIGS. 1-4 . Behavioral remedy 3020 is generated byidentifying at least a deterrent 3024 of a plurality of deterrents.Deterrent 3024 may be implemented, without limitation, as describedabove in reference to FIGS. 1-4 . Behavior remedy 3020 is generated bydetermining a collateral parameter 3028 related to deterrent 3024.Collateral parameter 3028 may be implemented, without limitation, asdescribed above in reference to FIGS. 1-4 . Behavioral remedy 3020 isgenerated as a function of the collateral parameter and a congestionvariable 3032. Congestion variable 3032 may be implemented, withoutlimitation, as described above in reference to FIGS. 1-4 .

Still referring to FIG. 33 , at step 3325 apparatus 3004 administers asa function of behavioral remedy 3020 deterrent 3024 that at least altersbehavior 3012 of the first individual. Administration of deterrent 3024may include performance of a first step in the initiation of adeterrent; first step may include a particular deterrent or signal, suchas an infrared laser and/or light output, a first entanglement device,or the like. First step may include location of a deterrent; locationmay include placement in an apparatus 3004. First step may includegeneration of a deterrent control; generation of a deterrent controlsystem may include transmission of a signal to initiate deterrent and/ortransmission of any deterrent controls generated as described above,including without limitation transmission of information for localizedand/or remote deterrent control. Transmission may be direct or indirect;for instance, transmission may involve transmission to a remote devicethat relays transmission to a deterrent or computing device coupledthereto, or transmission to an auxiliary computing device or computermemory for transport to the deterrent and/or computing device coupledthereto.

Referring Now to FIG. 32 , an exemplary embodiment 3200 of an apparatus3204 for determining an importance level 3208 is illustrated. Apparatus3204 is configured to identify an object to be protected. As used inthis disclosure an “object” is a material thing and/or person that has adimensional form and can be seen and/or touched. As a non-limitingexample and object to be protected may include a painting, jewel,heirloom, antiquity, valuable item, person, literary work, musical work,sculpture, royalty, government official, and the like thereof. An objectmay be identified by receiving at least a user input 3212 of a pluralityof user inputs. As used in this disclosure a “user input” includes anentry by an individual that is received by apparatus 3204 that at leastpertains to an object of interest. User input 3212 may include aselection from one or more graphical user interfaces and/or remotedevice 140, wherein remote device 140 includes all of remote device 140as described above, in reference to FIGS. 1-10 . As a non-limitingexample user input 3212 may include a security guard input a painting tobe protected. As a further non-limiting example user input 3212 mayinclude a secret service agent input of a dignitary such as a presidentor other head of state to be protected.

Further referring to FIG. 34 , apparatus 3404 may identify an object tobe protected as a function of obtaining at least a sensor datum 3416. Asused in this disclosure “sensor datum” comprises datum that relates toone or more external physical elements as captured by one or moresensors; one or more sensors may include a device that detects aphysical property such as a light sensor, an acoustic sensor, a chemicalsensor, and the like thereof. Sensor datum 3416 may include light,voltage, current, sound, chemical, pressure, humidity, and the likethereof. For example, and without limitation, sensor datum 3416 may becomprised of a pressure datum of 0.9821 atm. As a further non-limitingexample, sensor datum 3416 may include a chemical datum of 500 ppb ofN-Phenethyl-4-piperidone. Apparatus 3404 may identify an object bydetermining at least a recognition element from sensor datum 3416. Asused in this disclosure a “recognition element” is information obtainedfrom one or more sensor datum that relates to a discerning factorregarding an object and/or individual. As used in this disclosure a“discerning factor” is a unique quality, characteristic, and/or traitthat at least relates to an object and/or individual, such as a facialfeature, wherein a facial feature may include, without limitation eyes,nose, mouth, cheek bones, smile, and the like thereof. As a non-limitingexample, a recognition element may include defining features of anobjects and/or individuals such as color, brush stroke pattern,refractive index, style, and the like thereof. As a further non-limitingexample, a recognition element may include a biometric element relatingto the object and/or individual to be protected. As used in thisdisclosure a “biometric element” is a distinctive, measurablecharacteristic that at least labels and/or identifies an object to beprotected. A biometric element may include a physiologic characteristic.A physiological characteristic may relate to the shape and/or structureof the object and/or individual to be protected. For example, andwithout limitation a physiological characteristic may includefingerprint, palm veins, face recognition, DNA, palmprint, handgeometry, iris recognition, retina structure, odor, scent, dentalpatterns, weight, height, dermal viability, and the like thereof. As afurther non-limiting example a recognition element may relate to anobject's and/or individual's rhythm, gait, voice, typing pattern, typingspeed, device use patterns and the like thereof, wherein device usepatterns include cursor movements, finger pressure, finger contactduration, finger contact volume, finger contact angle, device angle whenoperating and the like thereof.

Still referring to FIG. 34 , recognition element may relate to one ormore identification elements. As used in this disclosure an“identification element” is datum and/or quality that at least uniquelydefines an individual. For example, and without limitation anidentification element may include a security clearance, a name, anidentification number, and the like thereof. As used in this disclosurea “security clearance” is a status granted to an individual allowing theindividual access to an otherwise restricted location, datum, or thelike, such as classified information or to restricted areas. Forexample, and without limitation a security clearance may include accessto a level 5 secure area, where there a total of 10 levels of security.As used in this disclosure a “name” is a word or set of words by whichan object may be known known, addressed, or referred to. For example,and without limitation a name may include common names, such as John,James, Robert, Michael, William, David, Richard, Mary, Patricia, Linda,Barbara. Elizabeth, Jennifer, and the like thereof. As used in thisdisclosure an identification number is any number or set of numbers bywhich an object may be identified. For example, and without limitation,a set of numbers may include social security number, telephone number,date of birth, residence zip code, and the like thereof. As used in thisdisclosure an “identification database” is a database, which may beimplemented in any manner suitable for implementation of a database asdescribed above, that at least stores, retains, and/or maintainsidentification elements of individuals. For example, and withoutlimitation, identification databases may include a National ID card,passport, social security death index, mail isolation control andtracking, integrated automated fingerprint identification system,combined DNA index system, investigate data warehouse, project MINARETwatch lists, NSA call database, TALON, Homeless Management InformationSystems, Case Management and Electronic Case Files, and the likethereof.

Still referring to FIG. 34 , apparatus 3404 may determine an importancelevel 3408 that relates to object to be protected. As used in thisdisclosure an “importance level” is a measurable value that relates tothe state and/or fact of an object's significance. As a non-limitingexample an importance level may determine a value of 20 for a vintageclock, wherein an importance level may determine a value of 95 for aunique piece of artwork, such as the Mona Lisa. As a furthernon-limiting example an importance level of 15 may be determined for asecurity guard in an area, wherein an importance level of 65 may bedetermined for the president of the United States. Importance level 3408may be determined by receiving an originality metric 3420 as a functionof the object being protected. As used in this disclosure an“originality metric” is a value of the uniqueness of the object to eh beprotected. For example an originality metric may be identified as avalue of 97 for a unique piece of artwork, wherein only one originalpiece exists in the world, wherein an originality metric may beidentified as a value of 2 for a vehicle, of which 200,000 vehicles wereproduced a year. Importance level 3408 may be determined by generatingan expendable element 3424 as a function of originality metric 3420. Asused in this disclosure an “expendable element” is a parameter thatrelates to the significance of the object compared to the overallpurpose of the object, wherein the object may or may not be required. Asa non-limiting example expendable element 3424 may denote an object thatis replaceable, such as a cake and/or utensils. As a furthernon-limiting example expendable element 3424 may denote an object thatis irreplaceable, such as a family member and/or friend.

Still referring to FIG. 34 , importance level 3408 may be determined asa function of expendable element 3424 and at least an importance model.As used in this disclosure an “importance model” is a machine-learningmodel that will be performed by an apparatus and/or one or more remotedevices 140. The importance model may generate importance level outputsgiven data provided expendable elements inputs. The importance model maybe generated by one or more importance machine-learning processes thatapparatus 3404 and/or remote devices 140 may utilize. The importancemachine-learning processes include any supervised, unsupervised, orreinforcement machine-learning process that apparatus 3404 and/or one ormore remotes device 140 may or may not use in the determination of theimportance model. An importance machine-learning process may include,without limitation machine learning processes such as simple linearregression, multiple linear regression, polynomial regression, supportvector regression, ridge regression, lasso regression, elasticnetregression, decision tree regression, random forest regression, logisticregression, logistic classification, K-nearest neighbors, support vectormachines, kernel support vector machines, naive bayes, decision treeclassification, random forest classification, K-means clustering,hierarchical clustering, dimensionality reduction, principal componentanalysis, linear discriminant analysis, kernel principal componentanalysis, Q-learning, State Action Reward State Action (SARSA), Deep-Qnetwork, Markov decision processes, Deep Deterministic Policy Gradient(DDPG), or the like thereof. The importance machine-learning process maybe trained as a function of an importance training set. As used in thisdisclosure “importance training set” is a training set that correlatesat least an expendable element to at least an originality metric. As anon-limiting example, the importance training set may relate anexpendable element of replaceable for a mass-produced telecommunicationsdevice to an originality metric of 10. As a further non-limitingexample, the importance training set may relate an expendable element ofirreplaceable for the Hope Diamond to an originality metric of 99.

Still referring to FIG. 34 , apparatus 3404 may receive the importancemodel from remote device 140. Remove device 140 may include any remotedevice 140 as described above in reference to FIGS. 1-10 . Remote device140 may provide modifications to importance model. For example, andwithout limitation, a modification may include a firmware update, asoftware update, an importance model correction, and the like thereof.As a non-limiting example a software update may incorporate a newimportance model that relates an expendable element to a modifiedoriginality metric. As a further non-limiting example remote device 140may transmit a modified importance model, wherein the modifiedimportance model may relate new originality metrics to previouslyidentified expandable elements. Additionally or alternatively, theimportance model may be transmitted to remote device 140, wherein remotedevice 140 may update the importance training data and transmit anupdated importance model back to apparatus 3404. The updated importancemodel may be transmitted by remote device 140 and may be received byapparatus 3404 as a software update, firmware update, or correctedimportance machine-learning model. Additionally or alternatively, remotedevice 140 may provide importance model, wherein apparatus 3404transmits a signal, bit, datum, or parameter to remote device 140 andreceives the outputted determined importance level from the importancemodel on remote device 140.

Still referring to FIG. 34 , importance level 3408 may be determined bydetermining an at least support metric 3428. As used in this disclosure“support metric” is a parameter that at least relates to theavailability of supporting individuals to at least reach and/or provideaid in protecting the object as a function of time and/or distance. As anon-limiting example support metric 3428 may include a parameter such asback up is available in 15 minutes or back up is unavailable for 35minutes. Support metric 3428 may be determined as a function ofreceiving a support advisor 3432 from a support database 3436. As usedin this disclosure a “support advisor” is at least an individual and/orentity that provides assistance to apparatus 3404. As a nonlimitingexample support advisor 3432 may include a local authority, such as apark ranger, a police officer, a security guard, and the like thereof.As used in this disclosure “support database” is a datastore ofpotential support advisors that may or may not aid in protecting theidentified object. As a non-limiting example support database 3436 mayinclude local authorities, lifestyle coaches, psychologists, physicians,spiritual advisors, familial members, state authorities, federalauthorities, social workers, and the like thereof. Support metric 3428may be determined by identifying at least a geolocation of the at leastsupport advisor. As used in this disclosure “geolocation” is aparticular place and or position of a support advisor that is able to bereceived remotely via a remote communication. For example, and withoutlimitation the geolocation of a psychologist may indicate that thepsychologist is exactly 10.23 miles away from the secure area. As afurther non-limiting example a geolocation may indicate a specific townand or city that the support advisor may be located. As a furthernon-limiting example a geolocation may indicate that state policeofficers are 2 miles away from the secure location.

Still referring to FIG. 34 apparatus 3404 may be configured to determineat least a deterrent 3440, wherein deterrent 3440 is described above, inreference to FIGS. 1-10 . Deterrent 3440 may include without limitationa directed light, sound, electrical deterrent, neurostimulator,chemical, entanglement device, and the like thereof. Deterrent 3440 maybe determined by receiving a candidate deterrent 3444 of a plurality ofcandidate deterrents from a deterrent database 3448. As used in thisdisclosure “candidate deterrents” are a list and/or group of deterrentsthat at least fulfill the importance level. For example, and withoutlimitation, candidate deterrent 3444 may include a laser, directed whitelight, and strobe light pulses for an importance level of 90. As used inthis disclosure a “deterrent database” is a datastore relating to theplurality of deterrents an apparatus may or may not have access toutilize. As a non-limiting example a deterrent database 3448 may includedeterrents that at least utilize a light, sound, electric shock,neurostimulant, chemical, and/or entanglement deterrent. Apparatus 3404may identify an impact parameter 3452 that relates to importance level3408. As used in this disclosure “impact parameter” is an element thatrelates to the magnitude of invasiveness that a deterrent has on subject308 and/or surrounding individuals. For example, and without limitationimpact parameter 3452 may identify a very invasive deterrent ofentanglement device for a large importance level such as a jewel. As afurther non-limiting example, impact parameter 3452 may identify anon-invasive deterrent of audio signal for a low importance level suchas a pencil. Impact parameter 3452 may include at least a collateralimpact 3456. As used in this disclosure “collateral impact” is a valueassociated with the effects of the deterrent on the surroundingindividuals. As a non-limiting example collateral impact 3452 may denotea value of 7 for a laser pulse, wherein a value of 82 may exist for anaudio output.

Referring now to FIG. 35 , an exemplary embodiment of a method 3500 ofdetermining an importance level is illustrated. At step 3505, aprocessor 136 communicatively connected to an imaging device 104 and adeterrent component 152 including a directed light deterrent 156 detectsa subject 308 as a function of a detection of the subject 308 by theimaging device 104; this may be implemented, without limitation, asdescribed above in reference to FIGS. 1-11 . At step 3510, the processor136 identifies an object to be protected; this may be implemented,without limitation, as described above in reference to FIGS. 1-11 . Atstep 3515, the processor determines an importance level 3408; this maybe implemented, without limitation, as described above in reference toFIGS. 1-11 . At step 3520, the processor 136 selects a mode of a firstdeterrent mode and a second deterrent mode as a function of importancelevel 3408; this may be implemented, without limitation, as describedabove in reference to FIGS. 1-11 . At step 3525, the processor 136commands deterrent component 152 to perform an action of a firstdeterrent action and a second deterrent action as a function ofimportance level 3408, wherein the first deterrent action is distinctfrom the second deterrent action; this may be implemented, withoutlimitation, as described above in reference to FIGS. 1-11 .

Referring now to FIG. 36 , an exemplary embodiment of a system 3600 oftransmitting a notification is illustrated. System includes an apparatus3604. Apparatus 3604 may include any apparatus as described in thisdisclosure, including without limitation a microcontroller,microprocessor, digital signal processor (DSP) and/or system on a chip(SoC) as described in this disclosure. Apparatus may include, beincluded in, and/or communicate with a mobile device such as a mobiletelephone or smartphone. Apparatus 3604 may include a single apparatusoperating independently, or may include two or more apparatus operatingin concert, in parallel, sequentially or the like; two or more apparatusmay be included together in a single apparatus or in two or moreapparatus. Apparatus 3604 may interface or communicate with one or moreadditional devices as described below in further detail via a networkinterface device. Network interface device may be utilized forconnecting apparatus 3604 to one or more of a variety of networks, andone or more devices. Examples of a network interface device include, butare not limited to, a network interface card (e.g., a mobile networkinterface card, a LAN card), a modem, and any combination thereof.Examples of a network include, but are not limited to, a wide areanetwork (e.g., the Internet, an enterprise network), a local areanetwork (e.g., a network associated with an office, a building, a campusor other relatively small geographic space), a telephone network, a datanetwork associated with a telephone/voice provider (e.g., a mobilecommunications provider data and/or voice network), a direct connectionbetween two apparatus, and any combinations thereof. A network mayemploy a wired and/or a wireless mode of communication. In general, anynetwork topology may be used. Information (e.g., data, software etc.)may be communicated to and/or from a computer and/or an apparatus.Apparatus 3604 may include but is not limited to, for example, anapparatus or cluster of apparatus in a first location and a secondapparatus or cluster of apparatus in a second location. Apparatus 3604may include one or more apparatus dedicated to data storage, security,distribution of traffic for load balancing, and the like. Apparatus 3604may distribute one or more computing tasks as described below across aplurality of apparatus of apparatus, which may operate in parallel, inseries, redundantly, or in any other manner used for distribution oftasks or memory between apparatus. Apparatus 3604 may be implementedusing a “shared nothing” architecture in which data is cached at theworker, in an embodiment, this may enable scalability of system 3600and/or apparatus.

Apparatus 3604 may be designed and/or configured to perform any method,method step, or sequence of method steps in any embodiment described inthis disclosure, in any order and with any degree of repetition. Forinstance, apparatus 3604 may be configured to perform a single step orsequence repeatedly until a desired or commanded outcome is achieved;repetition of a step or a sequence of steps may be performed iterativelyand/or recursively using outputs of previous repetitions as inputs tosubsequent repetitions, aggregating inputs and/or outputs of repetitionsto produce an aggregate result, reduction or decrement of one or morevariables such as global variables, and/or division of a largerprocessing task into a set of iteratively addressed smaller processingtasks. Apparatus 3604 may perform any step or sequence of steps asdescribed in this disclosure in parallel, such as simultaneously and/orsubstantially simultaneously performing a step two or more times usingtwo or more parallel threads, processor cores, or the like; division oftasks between parallel threads and/or processes may be performedaccording to any protocol suitable for division of tasks betweeniterations. Persons skilled in the art, upon reviewing the entirety ofthis disclosure, will be aware of various ways in which steps, sequencesof steps, processing tasks, and/or data may be subdivided, shared, orotherwise dealt with using iteration, recursion, and/or parallelprocessing.

Still referring to FIG. 36 , apparatus 3604 is configured to obtain asensory input of a plurality of sensory inputs 3608. As used in thisdisclosure, a “sensory input” comprises is datum that relates to one ormore external physical elements as captured by one or more sensors; oneor more sensors may include a device that detects a physical propertysuch as a light sensor, an acoustic sensor, a chemical sensor, and thelike thereof. A physical element may comprise light, voltage, current,sound, chemical, pressure, humidity, and the like thereof. For example,and without limitation, a sensory input may be comprised of a pressuredatum of 0.9821 atm. As a further non-limiting example, a sensory inputmay be comprised of a chemical datum of 3700 ppb ofN-Phenethyl-4-piperidone. Sensory input 3608 is obtained from arecognition sensor 3612. As used in this disclosure a “recognitionsensor” is a sensor that recognizes actions and goals of one or moreagents and/or persons from a series of observations on the agents and/orpersons actions and the environmental conditions. Recognition sensory3612 may include, without limitation imaging sensors, such as opticalcameras, infrared cameras, 3D cameras, multispectral cameras,hyperspectral cameras, polarized cameras. is a device that detects ormeasures a physical property and records, indicates, or otherwiseresponds to the detected or measured physical property. Recognitionssensor 3612 may be comprised of one or more of imaging and othersensors, such as optical cameras, infrared cameras, 3D cameras,multispectral cameras, hyperspectral cameras, polarized cameras,chemical sensors, motion sensors, ranging sensors, light radarcomponent, such as lidar, detection or imaging using radio frequenciescomponent, such as radar, terahertz or millimeter wave imagers, seismicsensors, magnetic sensors, weight/mass sensors, ionizing radiationsensors, and/or acoustical sensors. Sensors may alternatively oradditionally include any device used as a sensor as described in U.S.Provisional App. Ser. No. 63/067,142.

Still referring to FIG. 36 , apparatus 3604 is configured to identify anindividual 3616 of a plurality of individuals as a function of theplurality of sensory inputs. As used in this disclosure an “individual”is a person. As a non-limiting example, individual 3616 may be a30-year-old male. As a further non-limiting example, individual 3616 mayinclude a 48-year-old female. Individual 3616 may be identified as afunction of one or more biometric elements. As used in this disclosure a“biometric element” is a distinctive, measurable characteristic that atleast labels and/or identifies an individual. A biometric element mayinclude a physiologic or behavioral characteristic. A physiologicalcharacteristic may relate to the shape and/or structure of theindividual's body. For example, and without limitation a physiologicalcharacteristic may include fingerprint, palm veins, face recognition,DNA, palmprint, hand geometry, iris recognition, retina structure, odor,scent, dental patterns, weight, height, dermal viability, and the likethereof. A behavioral characteristic may relate to the pattern ofbehavior of an individual. A behavioral characteristic may relate to,without limitation, rhythm, gait, voice, typing pattern, typing speed,device use patterns and the like thereof, wherein device use patternsinclude cursor movements, finger pressure, finger contact duration,finger contact volume, finger contact angle, device angle when operatingand the like thereof.

Still referring to FIG. 36 , apparatus 3604 is configured to determine abehavior 3620 of individual 3616 as a function of sensory input 3608. Asused in this disclosure a “behavior” is an action and mannerismperformed by an individual, organism, system, or artificial entities inconjunction with themselves or their environment, which includes theother systems or organisms around as well as the physical environment.Behavior 3620 may include, without limitation, overt behavior, whereinovert behavior is a visible type of behavior that can occur outside of ahuman being. Overt behavior may include, without limitation, eatingfood, riding a bicycle, playing football, walking in a secure area, orthe like thereof. Behavior 3620 may include, without limitation, covertbehavior, wherein covert behavior is not visible to another individual.Covert behavior may include, without limitation, thoughts, emotions,feelings, or the like thereof. Behavior 3620 may include, withoutlimitation, molecular behavior, wherein molecular behavior includesunexpected behavior that occurs without thinking. Molecular behavior mayinclude, without limitation, an individual that closes their eyes whensomething is about to interact with that individual's eyes. Behavior3620 may include, without limitation, molar behavior, wherein molarbehavior is a behavior that is identified in terms of the ultimate causeof history. Molar Behavior may include, without limitation, a personthat loves someone is merely exhibiting a pattern of loving behaviorover time, as love would be considered atomistic and must be looked inmore wholistic terms. Behavior 3620 may include, without limitation,voluntary behavior, wherein voluntary behavior is a type of behaviorthat depends on a human want, desire, wish, yearning, or the likethereof. Voluntary behavior may include, without limitation, walking,speaking, writing, striking, and the like thereof. Behavior 3620 mayinclude, without limitation, involuntary behavior, wherein involuntarybehavior is a behavior that naturally occurs without thinking. Voluntarybehavior may include, without limitation, breathing, blinking,swallowing, digestion, or the like thereof. Behavior 3620 may includebehavior that is considered to be positive, negative, and/or neutralbehavior. As used in this disclosure “positive behavior” is behaviorthat is perceived by another individual, organism, artificialintelligence, or entity as a good act. As a non-limiting examplepositive behavior may include altruistic behavior, caring behavior,compassionate behavior, considerate behavior, faithful behavior,impartial behavior, kind behavior, pleasant behavior, polite behavior,sincere behavior, and the like thereof. As used in this closure a“negative behavior” is behavior that is perceived by another individual,organism, artificial intelligence, or entity as a bad act. As anon-limiting example a negative behavior may include aggressivebehavior, argumentative behavior, bossy behavior, deceitful behavior,domineering behavior, flaky behavior, inconsiderate behavior,manipulative behavior, rude behavior, spiteful behavior, and the likethereof. As used in this disclosure “neutral behavior” is behavior thatis perceived by another individual, organism, artificial intelligence,or entity as a behavior that does not attempt to display any positive ornegative intentions. As a non-limiting example a neutral behavior mayinclude apathy behavior, indifference behavior, lack of convictionbehavior, and the like thereof.

Still referring to FIG. 36 , determining behavior 3620 further comprisesidentifying at least a behavioral element 3624. As used in thisdisclosure a “behavioral element” is information that at least relatesto a user's intended behavior decision. Behavioral element 3624 mayinclude, without limitation, microexpression, macroexpressions,language, tone, word selection, physiological actions, and the likethereof. As a non-limiting example, behavioral element 3624 may relate amicroexpression of a nose wrinkled with a negative behavior of disgust.Behavior 3620 is then determined as a function of behavior element 3624and sensory inputs 3608. Behavior 3620 may be determined as a functionof a behavior machine-learning model 3628. As used in this disclosure a“behavior machine-learning model” is a machine-learning model that usestraining data and/or training set to generate an algorithm that will beperformed by an apparatus and/or remote device to produce outputs givendata provided as inputs; this is in contrast to a non-machine-learningsoftware program where the commands to be executed are determined inadvance by a user and written in a programming language. Behaviormachine-learning model 3628 may consist of any supervised, unsupervised,or reinforcement machine-learning model that apparatus 3604 may or maynot use in the determination of the behavior. Behavior machine-learningmodel 3628 may include, without limitation machine-learning models suchas simple linear regression, multiple linear regression, polynomialregression, support vector regression, ridge regression, lassoregression, elasticnet regression, decision tree regression, randomforest regression, logistic regression, logistic classification,K-nearest neighbors, support vector machines, kernel support vectormachines, naive bayes, decision tree classification, random forestclassification, K-means clustering, hierarchical clustering,dimensionality reduction, principal component analysis, lineardiscriminant analysis, kernel principal component analysis, Q-learning,State Action Reward State Action (SARSA), Deep-Q network, Markovdecision processes, Deep Deterministic Policy Gradient (DDPG), or thelike thereof. Behavior machine-learning model 3628 may be trained as afunction of a behavior training set 3632. As used in this disclosure“behavior training set” is a training set that correlates at least asensory input to at least a behavioral element, wherein a sensoryelement comprises at least datum that relates to one or more externalphysical elements, as described above in detail, and a behavioralelement further comprises a datum that relates to a person's intendedaction, as described above in detail.

Still referring to FIG. 36 , apparatus 3604 may receive behaviormachine-learning model 3628 from a remote device 3636. As used in thisdisclosure a “remote device” is a computing system external to theapparatus that obtains and/or sends information relating to the behaviormachine-learning model. Remote device 3636 may provide modifications tothe behavior machine-learning model. For example, and withoutlimitation, a modification may be comprised of a firmware update, asoftware update, a behavior machine-learning correction, and the likethereof. As a non-limiting example a software update may incorporate anew behavior machine-learning model that relates to a sensory input of aplurality of sensory inputs to a modified behavioral element. As afurther non-limiting example a remote device may transmit a modifiedbehavior machine-learning model, wherein the modified behaviormachine-learning model may relate new behavioral elements to previouslyidentified sensory inputs of a plurality of sensory inputs. Additionallyor alternatively, behavior machine-learning model 3628 may betransmitted to remote device 3636, wherein remote device 3636 may updatethe behavior training data and transmit an updated behaviormachine-learning model back to apparatus 3604. The updated behaviormachine-learning model may be transmitted by remote device 3636 andreceived by apparatus 3604 as a software update, firmware update, orcorrected behavior machine-learning model. Additionally oralternatively, remote device 3636 may include the behaviormachine-learning model, wherein apparatus 3604 transmits a signal, bit,datum, or parameter to the remote device and receives the outputtedbehavior from the behavior machine-learning model on remote device 3636.

Still referring to FIG. 36 , behavior machine-learning model 3628 may begenerated as a function of a classifier. A “classifier,” as used in thisdisclosure is a machine-learning model, such as a mathematical model,neural net, or program generated by a machine-learning algorithm knownas a “classification algorithm,” as described in further detail below,that sorts inputs into categories or bins of data, outputting thecategories or bins of data and/or labels associated therewith. Aclassifier may be configured to output at least a datum that labels orotherwise identifies a set of data that are clustered together, found tobe close under a distance metric as described below, or the like.Apparatus 3604 and/or another device may generate a classifier using aclassification algorithm, defined as a process whereby apparatus 3604derives a classifier from training data. Classification may be performedusing, without limitation, linear classifiers such as without limitationlogistic regression and/or naive Bayes classifiers, nearest neighborclassifiers such as k-nearest neighbors classifiers, support vectormachines, least squares support vector machines, fisher's lineardiscriminant, quadratic classifiers, decision trees, boosted trees,random forest classifiers, learning vector quantization, and/or neuralnetwork-based classifiers.

Still referring to FIG. 36 , apparatus 3604 may be configured togenerate a classifier using a Naïve Bayes classification algorithm.Naïve Bayes classification algorithm generates classifiers by assigningclass labels to problem instances, represented as vectors of elementvalues. Class labels are drawn from a finite set. Naïve Bayesclassification algorithm may include generating a family of algorithmsthat assume that the value of a particular element is independent of thevalue of any other element, given a class variable. Naïve Bayesclassification algorithm may be based on Bayes Theorem expressed asP(A/B)=P(B/A) P(A)÷P(B), where P(A/B) is the probability of hypothesis Agiven data B also known as posterior probability; P(B/A) is theprobability of data B given that the hypothesis A was true; P(A) is theprobability of hypothesis A being true regardless of data also known asprior probability of A; and P(B) is the probability of the dataregardless of the hypothesis. A naive Bayes algorithm may be generatedby first transforming training data into a frequency table. Apparatus3604 may then calculate a likelihood table by calculating probabilitiesof different data entries and classification labels. Apparatus 3604 mayutilize a naive Bayes equation to calculate a posterior probability foreach class. A class containing the highest posterior probability is theoutcome of prediction. Naïve Bayes classification algorithm may includea gaussian model that follows a normal distribution. Naïve Bayesclassification algorithm may include a multinomial model that is usedfor discrete counts. Naïve Bayes classification algorithm may include aBernoulli model that may be utilized when vectors are binary.

With continued reference to FIG. 36 , apparatus 3604 may be configuredto generate a classifier using a K-nearest neighbors (KNN) algorithm. A“K-nearest neighbors algorithm” as used in this disclosure, includes aclassification method that utilizes feature similarity to analyze howclosely out-of-sample-features resemble training data to classify inputdata to one or more clusters and/or categories of features asrepresented in training data; this may be performed by representing bothtraining data and input data in vector forms, and using one or moremeasures of vector similarity to identify classifications withintraining data, and to determine a classification of input data.K-nearest neighbors algorithm may include specifying a K-value, or anumber directing the classifier to select the k most similar entriestraining data to a given sample, determining the most common classifierof the entries in the database, and classifying the known sample; thismay be performed recursively and/or iteratively to generate a classifierthat may be used to classify input data as further samples. Forinstance, an initial set of samples may be performed to cover an initialheuristic and/or “first guess” at an output and/or relationship, whichmay be seeded, without limitation, using expert input received accordingto any process as described herein. As a non-limiting example, aninitial heuristic may include a ranking of associations between inputsand elements of training data. Heuristic may include selecting somenumber of highest-ranking associations and/or training data elements.

With continued reference to FIG. 36 , generating k-nearest neighborsalgorithm may generate a first vector output containing a data entrycluster, generating a second vector output containing an input data, andcalculate the distance between the first vector output and the secondvector output using any suitable norm such as cosine similarity,Euclidean distance measurement, or the like. Each vector output may berepresented, without limitation, as an n-tuple of values, where n is atleast two values. Each value of n-tuple of values may represent ameasurement or other quantitative value associated with a given categoryof data, or attribute, examples of which are provided in further detailbelow; a vector may be represented, without limitation, in n-dimensionalspace using an axis per category of value represented in n-tuple ofvalues, such that a vector has a geometric direction characterizing therelative quantities of attributes in the n-tuple as compared to eachother. Two vectors may be considered equivalent where their directions,and/or the relative quantities of values within each vector as comparedto each other, are the same; thus, as a non-limiting example, a vectorrepresented as [5, 10, 15] may be treated as equivalent, for purposes ofthis disclosure, as a vector represented as [1, 2, 3]. Vectors may bemore similar where their directions are more similar, and more differentwhere their directions are more divergent; however, vector similaritymay alternatively or additionally be determined using averages ofsimilarities between like attributes, or any other measure of similaritysuitable for any n-tuple of values, or aggregation of numericalsimilarity measures for the purposes of loss functions as described infurther detail below. Any vectors as described herein may be scaled,such that each vector represents each attribute along an equivalentscale of values. Each vector may be “normalized,” or divided by a“length” attribute, such as a length attribute l as derived using aPythagorean norm: l=√{square root over (Σ_(i=0) ^(n)a_(i) ²)}, where a,is attribute number i of the vector. Scaling and/or normalization mayfunction to make vector comparison independent of absolute quantities ofattributes, while preserving any dependency on similarity of attributes;this may, for instance, be advantageous where cases represented intraining data are represented by different quantities of samples, whichmay result in proportionally equivalent vectors with divergent values.

Still referring to FIG. 36 , apparatus 3604 is configured to generate abehavior value 3640 as a function of behavior 3620. As used in thisdisclosure a “behavior value” is a measurable enumeration thatquantitates an impact of the behavior. As used in this disclosure an“impact” of a behavior is an effect the behavior has on the individualas well as surrounding individuals that are at least within a proximaldistance. As a non-limiting example a behavior value of 3600 may begenerated for a behavior of murder, while a behavior value of 10 may beidentified for a behavior of verbal abuse. Behavior value 3640 may begenerated as a function of a behavior grouping element 3644. As used inthis disclosure a “behavior grouping element” relates a type of actionand/or goal of a behavior to related actions and or goals of similarbehavior types. For example, and without limitation a behavior ofattempted suicide may be related to a behavior of murder. As a furthernon-limiting example a behavior of compliments may relate to kindness.Behavior grouping element 3644 identifies categories of behaviors suchthat a measurable value may be accomplished. Apparatus 3604 may arrangethe datum from behavior 3620 according to behavior grouping element3644.

Still referring to FIG. 36 , behavior grouping element 3644 may bedetermined as a function of a grouping machine-learning model 3648. Asused in this disclosure a “grouping machine-learning model” is amachine-learning model that uses training data and/or training set togenerate an algorithm that will be performed by an apparatus and/orremote device to produce outputs given data provided as inputs; this isin contrast to a non-machine-learning software program where thecommands to be executed are determined in advance by a user and writtenin a programming language. Grouping machine-learning model 3648 mayconsist of any supervised, unsupervised, or reinforcementmachine-learning model that apparatus 3604 may or may not use in thedetermination of the behavioral grouping element. Behaviormachine-learning model 3628 may include, without limitationmachine-learning models such as simple linear regression, multiplelinear regression, polynomial regression, support vector regression,ridge regression, lasso regression, elasticnet regression, decision treeregression, random forest regression, logistic regression, logisticclassification, K-nearest neighbors, support vector machines, kernelsupport vector machines, naive bayes, decision tree classification,random forest classification, K-means clustering, hierarchicalclustering, dimensionality reduction, principal component analysis,linear discriminant analysis, kernel principal component analysis,Q-learning, State Action Reward State Action (SARSA), Deep-Q network,Markov decision processes, Deep Deterministic Policy Gradient (DDPG), orthe like thereof. Grouping machine-learning model 3648 may be trained asa function of a grouping training set 3652. As used in this disclosure“grouping training set” is a training set that correlates at least abehavior to a behavior rating scale, wherein a behavior is an actionand/or goal of a person, as described in detail above. As used in thisdisclosure a “behavior rating scale” is a measurable index of eachnegative, positive, and/or neutral behavior. A behavioral rating scalemay be further comprised of Observer/Informant scales, self-reportscales, single domain scales, multidomain scales, Achenbach scales,CBCL, TRF, C-TRF/1-5, YSR, ABCL, ASR, BASC-2, PRS, TRS, SRP, ConnersScale, BRP-2, BBRS, SEDS-2, and the like thereof. As a non-limitingexample grouping training set 3652 may relate a CBCL value of 2 to abehavior of oppositional defiance.

Still referring to FIG. 36 , apparatus 3604 may receive groupingmachine-learning model from a remote device 3636. Remote device 3636 mayprovide grouping machine-learning model using one or more groupingmachine-learning models, wherein a grouping machine-learning model isdescribed above in detail. Remote device 3636 may perform groupingmachine-learning model 3648, using a grouping training set 3652, whereina grouping training set 3652 is described above in detail. The remotedevice may transmit a signal, bit, datum, or parameter to apparatus 3604that at least relates to grouping machine-learning model 3648. Remotedevice 3636 may provide modifications to the grouping machine-learningmodel. For example, and without limitation, a modification may becomprised of a firmware update, a software update, a groupingmachine-learning correction, and the like thereof. As a non-limitingexample a software update may incorporate a new groupingmachine-learning model that relates to a behavior and a modifiedbehavior rating scale. As a further non-limiting example a remote devicemay transmit a modified grouping machine-learning model, wherein themodified grouping machine-learning model may relate new behaviors topreviously identified behavioral rating scales. Additionally oralternatively, grouping machine-learning model 3648 may be transmittedto remote device 3636, wherein remote device 3636 may update thegrouping training data and transmit an updated grouping machine-learningmodel back to apparatus 3604. The updated grouping machine-learningmodel may be transmitted by remote device 3636 and received by apparatus3604 as a software update, firmware update, or corrected groupingmachine-learning model. Additionally or alternatively, remote device3636 may include the grouping machine-learning model, wherein apparatus3604 transmits a signal, bit, datum, or parameter to the remote deviceand receives the outputted behavior grouping element from the groupingmachine-learning model on remote device 3636.

Still referring to FIG. 36 , apparatus 3604 may generate behavior value3640 by identifying a vector outcome. As used in this disclosure a“vector outcome” is an action and/or response that an individual maycomplete as an effect of the identified behavior. For example, andwithout limitation, a vector outcome of opening a door for an individualmay be predicted as a function of an identified behavior of kindness. Asa further non-limiting example a vector outcome of assault may bepredicted as a function of the behavior of verbal discourse among anindividual. A vector outcome may be identified as a function of a vectormodel. As used in this disclosure a “vector model” is a logicalalgorithm consisting of a many-valued logic function. A many-valuedlogic function may include, without limitation, a propositional calculusfunction, wherein there are more than two truth values that may existfor a given input. For example, and without limitation, functions mayinclude two-valued logic, n-valued logic, three-valued Lukasiewicz's andKleene's logic, finite-valued logic, infinite-valued logic, fuzzy logic,and probability logic.

Still referring to FIG. 36 , a vector outcome may be identified byreceiving an actual outcome of individual 3616 from recognition sensor3612. As used in this disclosure an “actual outcome” a true responsefrom an individual that occurs in real-time. For example, an actualoutcome may include an individual that at least commits theft as aresult of a behavior of suspiciousness. As a further non-limitingexample an actual outcome may include a handshake as a behavior ofrespect for an individual. Apparatus 3604 may correlate the actualoutcome to a psyche databank. As used in this disclosure a “psychedatabank” is an online datastore of psychological status's that anindividual may or may not represent. As a non-limiting example a psychedatabank may include a list of personalities, wherein an individual mayhave a personality of cheery. Apparatus 3604 may generate the vectoroutcome using statistical inference. As used in this disclosure a“statistical inference” is a process that uses data analysis to deduceproperties of an underlying distribution probability. As a non-limitingexample a statistical inference may include fully parametric,non-parametric, semi-parametric, approximate distributions,randomization-based models, model-based analysis of randomizedexperiments, model-free randomization inference, frequentist inference,Bayesian inference, Likelihood-based inference, AIC-based inference,minimum description length, fiducial inference, structural inference,and the like thereof.

Still referring to FIG. 36 , apparatus 3604 is configured to determine athreshold constraint 3656. As used in this disclosure a “thresholdconstraint” is a value and/or set of values that that may not beexceeded for a given behavior value such that a signal is transmitted ifa behavior value exceeds the value of the threshold limit. Thresholdconstraint may be reached by a behavior value for example and withoutlimitation when a behavior value exceeds what the apparatus can handle,either because of a higher level of threat to whatever is beingprotected or to the apparatus itself, or because the situation requiresa trained professional that may be more qualified to handle thesituation than the apparatus is equipped to provide. For example, andwithout limitation, a threshold constraint may identify that a behaviorvalue should not exceed a value of 30 for a behavior associated withaggression. As a further non-limiting example a threshold constraint mayidentify that a behavior value should not be inferior to the value of 20for a behavior associated with goodwill. Additionally or alternatively,a person on the Autistic spectrum may be exhibiting a behavior valuethat exceeds the threshold constraint, wherein no threat is presented,wherein a trained professional would need to be notified of the behaviorvalue that exceeds the threshold constraint to at least intervene andsafely subdue the elevated behavior classifier. Threshold constraint3656 may be determined using a threshold qualifier 3660. As used in thisdisclosure a “threshold qualifier” is an algorithm and/or classifierthat at least relates a behavior value, wherein a behavior value is ameasurable enumeration relating to a behavior, as described in detailabove, to a socially approved limit. As used in this disclosure a“societal behavior” is a behavior limit that is decided by a society asa function of customs, laws, regulations, or other social constructsand/or conditioning. For example, and without limitation a thresholdqualifier may relate a behavior value of 20 to a societal behavior ofverbal abuse. As a further non-limiting example, a threshold qualifiermay relate a behavior value of 40 to suspicious behavior. Additionallyor alternatively, a threshold qualifier may relate a behavior value of200 for an individual that has improper security clearance to a securearea. Threshold qualifier 3660 may be comprised of one or morealgorithms that at least aid in identifying threshold constraint 3656.For example, and without limitation, threshold qualifier 3660 mayinclude heuristic algorithms. As used in this disclosure “heuristicalgorithms” are algorithms that enhance speed of calculations by whilesacrificing relatively little accuracy, such that a threshold constraintmay be rapidly determined with prior to another behavior being conductedby the individual. In an embodiment, using at least a heuristicalgorithms to determine a threshold constraint may drastically reducethe time needed to perform the determination, while reducing a minisculeamount of accuracy; for example heuristic determination of thresholdconstraints may be several factors of ten faster than brute forceapproaches. In other embodiments, heuristic algorithms may include aheuristic algorithm based on a single variable or a small set ofvariables, such as a single behavior of theft or trespass. Thresholdqualifier 3660 may include fuzzy logic algorithms. As used in thisdisclosure “fuzzy logic algorithms” are processes that identify truthvalues within a specified range of true and false values. For instanceand without limitation, fuzzy logic algorithm may identify a truth valueof 0.14 for a set range of 0 and 1 for respective truth and falsevalues.

Still referring to FIG. 36 , apparatus 3604 is configured to transmit anotification 3664 as a function of behavior value 3640 and thresholdconstraint 3656. As used in this disclosure a “notification” is anindicator that an event has occurred. Notification 3664 may include,without limitation an electrical signal, bit, noise, light, flash,siren, ballistic, and the like thereof. Notification 3664 may transmit asignal to an external remote device such as local authorities, such thatthe local authorities may respond to the notification and mitigate thebehavior value that is exceeding the threshold constraint. For example,and without limitation, an individual may be trespassing in a securearea, wherein a notification consisting of a text and/or phone call maybe transmitted to the local, state, and federal authorities. As afurther non-limiting example, a video of the comprising the behavior maybe recorded and transmitted to the local authorities to at least providethe local authorities a preview of the behavior being presented toapparatus 3604. Additionally or alternatively, a notification consistingof a text message with a description of the system may be identifiedallowing the local authorities to select the notification and watch alive-video feed displaying the situation at hand. Notification 3664 maybe transmitted to a specific entity, device, remote device, individual,and the like thereof. Notification 3664 may indicate specificinstructions or recommendations as a function of the behavior value. Forinstance, and without limitation, a notification may state that atrained negotiator needs to response such that the trained professionalcan provide cognitive bias mitigation and/or de-escalation techniques.Notification 3664 may include the individual's current behavior andcurrent objects and/or weapons that the individual possess. For example,a notification may indicate to the local authorities that an individualthat at least violates the threshold constraint due to trespass has boltcutters, a firearm, and a knife. Notification 3664 may relate to abehavior that at least violates a threshold constraint. As used in thisdisclosure a “violation” is a behavior value that at least exceeds orfalls below the range of values established by the threshold constraint.A violation may further comprise receiving behavior value 3640 ofbehavior 3620, receiving threshold constraint 3656 relating to behavior3620, and determining a violation relating behavior 3620 and thresholdconstraint 3656 as a function of a behavior algorithm. As used in thisdisclosure a “behavior algorithm” is a mathematical formula that atleast relates a value to another value and/or range of values. As anon-limiting example, a behavior algorithm may include additionformulas, subtraction formulas, lattice formulas, scratch formulas, andthe like thereof.

Now referring to FIG. 37 , an exemplary embodiment of a system 3700 forgenerating a behavior value 3640 as a function of vector outcome 3704,wherein vector outcome 3704 is an action that an individual may completeas an effect of the identified behavior, described in detail above. Forexample, and without limitation, vector outcome 3704 may be comprised ofa predicted smile as a function of an identified behavior of empathy. Asa further non-limiting example vector outcome 3704 may predict verbalabuse as a function of the behavior of disgust. Vector outcome may begenerated as a function of a vector model 3708, wherein a vector modelis a logical algorithm consisting of a many-valued logic function, asdescribed in detail above. As a non-limiting example, a vector model mayinclude fuzzy logic and/or probability logic to generate vector outcome3704. Vector model 3708 may be identified as a function of a statisticalinference 3712, wherein a statistical inference is a process that usesdata analysis to deduce properties of an underlying distributionprobability, as described above in detail. As a non-limiting example astatistical inference may include a Likelihood-based inference relatingan actual outcome 3716, received by recognition sensor 3612, and apsyche databank 3720. An actual outcome 3716 is a true response from anindividual that occurs in real-time, as described above in detail. Forexample, and without limitation actual outcome 3716 may include assaultas a function of a behavior of aggression. Psyche databank 3720 is anonline datastore of psychological status's that an individual may or maynot represent, as described above in detail. As a non-limiting example apsyche databank may relate an actual outcome to a psyche type such asthinker.

Now referring to FIG. 38 , an exemplary embodiment 3800 of a psychedatabank 3804 is illustrated. Databank may be implemented, withoutlimitation, as a relational databank, a key-value retrieval databanksuch as a NOSQL databank, or any other format or structure for use as adatabank that a person skilled in the art would recognize as suitableupon review of the entirety of this disclosure. Databank mayalternatively or additionally be implemented using a distributed datastorage protocol and/or data structure, such as a distributed hash tableor the like. Databank may include a plurality of data entries and/orrecords as described above. Data entries in a databank may be flaggedwith or linked to one or more additional elements of information, whichmay be reflected in data entry cells and/or in linked tables such astables related by one or more indices in a relational database. Personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various ways in which data entries in a databank may store,retrieve, organize, and/or reflect data and/or records as used herein,as well as categories and/or populations of data consistently with thisdisclosure. Psyche databank 3804 may include a Theory of Mind tableset3808, which may indicate an individual's psyche as a function of mentalstates. This may include, without limitation a beliefs, intent, desire,emotion, knowledge, and the like thereof. Theory of Mind tableset 3808may include perspectives that relate to everyday human socialinteractions, for analyzing, judging, and inferring behaviors. Forexample, and without limitation, Freudian tableset 3808 may relate abehavior of verbal abuse to a psyche of aggressive id dominant. Psychedatabank 3804 may include a personality tableset 3812, which mayindicate an individual's psyche as a function of five types ofpersonalities, being a Type A, Type B, Type C, Type D, and/or Type X.This may include, without limitation a director psyche, a socializerpsyche, a thinker psyche, and a support psyche. For example, and withoutlimitation, personality tableset 3812 may relate a behavior ofenthusiasm to a psyche a socializer. Psyche databank 3804 may include apersonality tableset 3816, which may indicate an individual's psyche asa function of two perceiving functions and two judging functions. Thismay include, without limitation a sensation psyche, intuition psyche,thinking psyche, and/or feeling psyche. For example, and withoutlimitation, psychological tableset 3816 may relate a behavior of lack ofattention to a thinking psyche. Psyche databank 3804 may include atemperament tableset 3820, which may indicate an individual's psyche asa function of four temperament types. This may include, withoutlimitation a sanguine type, choleric type, melancholic type, and/orphlegmatic type. For example, and without limitation, temperamenttableset 3820 may relate a behavior of lack of individual movement to anactive and/or sanguine temperament.

Now Referring to FIG. 39 , an exemplary embodiment of a method 3900 fortransmitting a notification. At step 3905, an apparatus 3604 obtains asensory input 3608. Sensory input 3608 includes any of the sensory input3608 as described above, in reference to FIGS. 1-4 . For instance, andwithout limitation, sensory input 3608 may include a light, voltage,current, sound, chemical, pressure, humidity, and the like thereof.Sensory input is obtained from a recognition sensor 3612. Recognitionsensor 3612 includes any of the recognition sensor 3612 as describedabove, in reference to FIGS. 1-4 . Recognition sensor 3612 may includeone or more of imaging and other sensors, such as optical cameras,infrared cameras, 3D cameras, multispectral cameras, hyperspectralcameras, polarized cameras, chemical sensors, motion sensors, rangingsensors, light radar component, such as lidar, detection or imagingusing radio frequencies component, such as radar, terahertz ormillimeter wave imagers, seismic sensors, magnetic sensors, weight/masssensors, ionizing radiation sensors, and/or acoustical sensors.

Still referring to FIG. 39 , at step 3910 apparatus 3604 identifies anindividual 3616 as a function of sensory input 3608. Individual 3616includes any of the individual 3616 as described above, in reference toFIGS. 1-4 . Individual 3616 may include a person, entity, organism, andthe like thereof. Individual may be identified as a function of abiometric element, wherein a biometric element is a distinctive,measurable characteristic that at least labels and/or identifies anindividual. As a non-limiting example a biometric element may consist ofdatum obtained from a fingerprint, palm veins, face recognition, DNA,palmprint, hand geometry, iris recognition, retina structure, odor,scent, dental patterns, weight, height, dermal viability, rhythm, gait,voice, typing pattern, typing speed, device use patterns and the likethereof.

Still referring to FIG. 39 , at step 3915 apparatus 3604 determines abehavior 3620. Behavior 3620 includes any of the behavior 3620 asdescribed above, in reference to FIGS. 1-4 . For instance Behavior 3620may include overt behavior, covert behavior, molar behavior, molecularbehavior, voluntary behavior, involuntary behavior, and the likethereof. Behavior 3620 may be comprised of positive behavior, negativebehavior, and/or neutral behavior. Behavior 3620 is determined byidentifying at least a behavioral element 3624. Behavioral element 3624includes any of the behavioral element 3624 as described above indetail. For example, and without limitation a physiological action oftrespassing may relate to a behavioral element of negative behavior.Behavior 3620 is then determined as a function of behavior element 3624and sensory inputs 3608. Behavior 3620 may be identified as a functionof a behavior machine-learning model 3628. Behavior machine-learningmodel 3628 includes any of the behavior machine-learning model 3616 asdescribed above, in reference to FIGS. 1-4 . For instance, and withoutlimitation, behavior machine-learning model 3628 may include asupervised machine-learning model or an unsupervised machine-learningmodel. Behavior machine-learning model 3628 may include a classificationprocess, such as for example naive Bayes, k-nearest neighbor, decisiontree, and/or random forest. Classification processes include any of theclassification processes as described above in reference to FIGS. 1-4 .Behavior machine-learning model 3628 may be configured using a behaviortraining set 3632. Behavior training set 3632 includes any of thebehavior training set 3632 as described above in reference to FIGS. 1-4. Behavior training set 3632 may include, without limitation, sensoryinputs, such as movements, language, intentions, and the like thereofthat correlate to behavioral elements, such as intended behaviordecisions. For example, and without limitation a behavior training setmay relate a sensory input of perceived microexpression to a negativebehavior of disgust.

Still referring to FIG. 39 , at step 3920 apparatus 3604 generates abehavior value as a function of behavior 3620. Behavior value 3640includes any of the behavior value 3640 as described above, in referenceto FIGS. 1-4 . As a non-limiting example a behavior value of 40 may begenerated for a behavior of derogatory behavior, while a behavior valueof 5 may be identified for a behavior of ignorance. Behavior value 3640may be generated as a function of a behavior grouping element 3644.Behavior grouping element 3644 Includes any of the behavior groupingelement 3644 As described above, in reference to FIGS. 1-4 . Forinstance and without limitation, behavior grouping 3644 may include atype of action and/or goal of a behavior to related actions and or goalsof similar behavior types. As a non-limiting example, a behaviorgrouping element may relate a behavior of attempted suicide may berelated to a behavior of murder. Behavior grouping element 3644 may begenerated as a function of a grouping machine-learning model 3648.Grouping machine-learning model 3648 includes any of the groupingmachine-learning model 3648 as described above in reference to FIGS. 1-4. For instance, and without limitation, grouping machine-learning model3648 may include a supervised machine-learning model or an unsupervisedmachine-learning model. Grouping machine-learning model 3648 may includea classification process, such as for example naive Bayes, k-nearestneighbor, decision tree, and/or random forest. Classification processesinclude any of the classification processes as described above inreference to FIGS. 1-4 . Grouping machine-learning model 3648 may beconfigured using a grouping training set 3652. Grouping training set3652 includes any of the grouping training set 3652 as described abovein reference to FIGS. 1-4 . Grouping training set 3652 may include,without limitation, a behavior of a plurality of behaviors related to abehavior rating scale, wherein a behavior rating scale is a measurableindex of each negative, positive, and/or neutral behavior. For example,and without limitation a grouping training set may relate an Achenbachscale value of 3 to a behavior of somatic complaints.

Still referring to FIG. 39 , at step 3925 apparatus 3604 determines athreshold constraint 3656. Threshold constraint 3656 includes any of thethreshold constraint 3656 as described above, in reference to FIGS. 1-4. As a non-limiting example threshold constraint 3656 may include avalue range of 1-30 for a behavior associated with depression. Thresholdconstraint 3656 may be determined as a function of a threshold qualifier3660. Threshold qualifier 3660 includes any of the threshold constraint3660 as described above in reference to FIGS. 1-4 . Threshold qualifier3660 may include, without limitation one or more algorithms that atleast aid in identifying threshold constraint 3656. For example, andwithout limitation, threshold qualifiers may include heuristicalgorithms and/or fuzzy logic algorithms. As a further non-limitingexample, a threshold qualifier may identify a truth value of 0.12 for aset range of 0 and 1 for a probability using a heuristic algorithm.

Still referring to FIG. 39 , at step 3930 transmits a notification 3664.Notification 3664 includes any of the notification 3664 as describedabove, in reference to FIGS. 1-4 . Notification 3664 may include anindicator that is emitted as result of an event that occurred.Notification 3664 may include, without limitation an electrical signal,bit, noise, light, flash, siren, ballistic, and the like thereof.Notification 3664 may relate to a behavior that at least violates athreshold constraint, wherein a violation relates to any violation asdescribed above, in reference to FIGS. 1-4 . As a non-limiting example,a violation may result from a behavior value of 30 and a thresholdconstraint of 1-25. A violation may be determined as a function of abehavior algorithm, wherein a behavior algorithm includes any of thebehavior algorithm as described above, in reference to FIGS. 1-4 . As anon-limiting example a behavior algorithm may include addition formulas,subtraction formulas, lattice formulas, scratch formulas, and the likethereof.

Referring now to FIG. 40 , an exemplary embodiment of a system 4000 foran automated threat detection and deterrence apparatus is illustrated.System includes an automated threat detection and deterrence apparatus4004. Apparatus 4004 may include any computing device as described inthis disclosure, including without limitation a microcontroller,microprocessor, digital signal processor (DSP) and/or system on a chip(SoC) as described in this disclosure. Computing device may include, beincluded in, and/or communicate with a mobile device such as a mobiletelephone or smartphone. Apparatus 4004 may include a single computingdevice operating independently, or may include two or more computingdevice operating in concert, in parallel, sequentially or the like; twoor more computing devices may be included together in a single computingdevice or in two or more computing devices. Apparatus 4004 may interfaceor communicate with one or more additional devices as described below infurther detail via a network interface device. Network interface devicemay be utilized for connecting apparatus 4004 to one or more of avariety of networks, and one or more devices. Examples of a networkinterface device include, but are not limited to, a network interfacecard (e.g., a mobile network interface card, a LAN card), a modem, andany combination thereof. Examples of a network include, but are notlimited to, a wide area network (e.g., the Internet, an enterprisenetwork), a local area network (e.g., a network associated with anoffice, a building, a campus or other relatively small geographicspace), a telephone network, a data network associated with atelephone/voice provider (e.g., a mobile communications provider dataand/or voice network), a direct connection between two computingdevices, and any combinations thereof. A network may employ a wiredand/or a wireless mode of communication. In general, any networktopology may be used. Information (e.g., data, software etc.) may becommunicated to and/or from a computer and/or a computing device.Apparatus 4004 may include but is not limited to, for example, acomputing device or cluster of computing devices in a first location anda second computing device or cluster of computing devices in a secondlocation. Apparatus 4004 may include one or more computing devicesdedicated to data storage, security, distribution of traffic for loadbalancing, and the like. Apparatus 4004 may distribute one or morecomputing tasks as described below across a plurality of computingdevices of computing device, which may operate in parallel, in series,redundantly, or in any other manner used for distribution of tasks ormemory between computing devices. Apparatus 4004 may be implementedusing a “shared nothing” architecture in which data is cached at theworker, in an embodiment, this may enable scalability of system 4000and/or computing device.

With continued reference to FIG. 40 , apparatus 4004 may be designedand/or configured to perform any method, method step, or sequence ofmethod steps in any embodiment described in this disclosure, in anyorder and with any degree of repetition. For instance, apparatus 4004may be configured to perform a single step or sequence repeatedly untila desired or commanded outcome is achieved; repetition of a step or asequence of steps may be performed iteratively and/or recursively usingoutputs of previous repetitions as inputs to subsequent repetitions,aggregating inputs and/or outputs of repetitions to produce an aggregateresult, reduction or decrement of one or more variables such as globalvariables, and/or division of a larger processing task into a set ofiteratively addressed smaller processing tasks. Apparatus 4004 mayperform any step or sequence of steps as described in this disclosure inparallel, such as simultaneously and/or substantially simultaneouslyperforming a step two or more times using two or more parallel threads,processor cores, or the like; division of tasks between parallel threadsand/or processes may be performed according to any protocol suitable fordivision of tasks between iterations. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of various waysin which steps, sequences of steps, processing tasks, and/or data may besubdivided, shared, or otherwise dealt with using iteration, recursion,and/or parallel processing.

Still referring to FIG. 40 , apparatus 4004 includes an imaging device4008. As used in this disclosure an “imaging device” is an instrumentcapable of recording, storing, viewing, and/or transmitting visualimages. As a non-limiting example, imaging device 4008 may include anoptical camera. As used in this disclosure an “optical camera” is adevice that generates still and/or video images by capturing senseselectromagnetic radiation in the visible spectrum, having wavelengthsbetween approximately 380 nm and 740 nm, which radiation in this rangemay be referred to for the purposes of this disclosure as “visiblelight,” wavelengths approximately between 740 nm and 1,100 nm, whichradiation in this range may be referred to for the purposes of thisdisclosure as “near-infrared light” or “NIR,” and wavelengthsapproximately between 200 nm and 380 nm, which radiation in this rangemay be referred to for the purposes of this disclosure as “ultravioletlight” or “UV”. Optical camera may include a plurality of opticaldetectors, visible photodetectors, or photodetectors, where an “opticaldetector,” “visible photodetector,” or “photodetector” is defined as anelectronic device that alters any parameter of an electronic circuitwhen contacted by visible or NIR light. Optical detectors may include,without limitation, charge-coupled devices (CCD), photodiodes, avalanchephotodiodes (APDs), silicon photo-multipliers (SiPMs), complementarymetal-oxide-semiconductor (CMOS), scientific CMOS (sCMOS), micro-channelplates (MCPs), micro-channel plate photomultiplier tubes (MCP-PMTs),single photon avalanche diode (SPAD), Electron Bombarded Active PixelSensor (EBAPS), quanta image sensor (QIS), spatial phase imagers (SPI),quantum dot cameras, image intensification tubes, photovoltaic imagers,optical flow sensors and/or imagers, photoresistors and/orphotosensitive or photon-detecting circuit elements, semiconductorsand/or transducers. APDs, as used herein, are diodes (e.g. withoutlimitation p-n, p-i-n, and others) reverse biased such that a singlephoton generated carrier can trigger a short, temporary “avalanche” ofphotocurrent on the order of milliamps or more caused by electrons beingaccelerated through a high field region of the diode and impact ionizingcovalent bonds in the bulk material, these in turn triggering greaterimpact ionization of electron-hole pairs. APDs may provide a built-instage of gain through avalanche multiplication. When a reverse bias isless than breakdown voltage, a gain of an APD may be approximatelylinear. For silicon APDs this gain may be on the order of 10-100. Thematerial of the APD may contribute to gains.

Still referring to FIG. 40 , individual photodetectors in optical cameramay be sensitive to specific wavelengths of light, for instance by useof optical filters to exclude such wavelengths; for instance, andwithout limitation, some photodetectors may be sensitive to blue light,defined as light having a wavelength of approximately 420 nm to 480 nm,some may be sensitive to green light, defined as light having awavelength of approximately 534 nm to 545 nm, and some may be sensitiveto red light, defined as light having a wavelength of approximately 564nm to 580 nm. Combinations of photodetectors specifically sensitive tored, green, and blue wavelengths may correspond to wavelengthsensitivity of human retinal cone cells, which detect light in similarfrequency ranges. Photodetectors may be grouped into a three-dimensionalarray of pixels, each pixel including a red photodetector, a bluephotodetector, and a green photodetector. Pixels may be small enough tofit millions into a rectangular array less than an inch across. Opticalcamera may include one or more reflective, diffractive, refractive,and/or adaptive components that focus incident light ontophotodetectors.

Still referring to FIG. 40 , imaging device 4008 may include an infraredcamera. As used in this disclosure an “infrared camera” is a camera thatdetects electromagnetic radiation in the infrared spectrum, defined as aspectrum of electromagnetic radiation having wavelengths betweenapproximately 740 nm and 14.0 μm, which radiation in this range may begenerally referred to for the purposes of this disclosure as “infraredlight,”. As non-limiting examples, infrared camera may detect light inthe 1.0 to 3.0 μm range, which radiation in this range may be referredto for the purposes of this disclosure as “shortwave infrared light” or“SWIR,” may detect light in the 3.0 to 5.0 μm range, which radiation inthis range may be referred to for the purposes of this disclosure as“midwave infrared light” or “MWIR,” or may detect light in the 8.0 to14.0 μm range, which radiation in this range may be referred to for thepurposes of this disclosure as “longwave infrared light” or “LWIR.”Infrared camera may include a plurality of infrared detectors orinfrared photodetectors, where an “infrared detector” or “infraredphotodetector” is defined as an electronic device that device thatalters any parameter of an electronic circuit when contacted by infraredlight. Infrared detectors may include, without limitation, siliconphotodiodes doped to detect infrared light, strained-layer super lattice(SLS) photodetectors, quantum well infrared photodetectors (QWIP),amorphous silicon (αSi) photodetectors, Vanadium Oxide (VOx)microbolometers, Barium Strontium Titanate (BST), thermopile arraydetector, pyroelectric infrared detectors, detectors constructed fromnarrow bandgap detector materials from the III-V elemental group, and/orother infrared photoelectric, photovoltaic and/or microbolometer baseddetectors. A “microbolometer” is defined for the purposes of thisdisclosure as a specific type of bolometer used as a detector in a LWIRcamera, also known as a “thermal camera.” Microbolometer may detectinfrared light when infrared radiation with wavelengths between 7.5-14μm strikes detector material, heating it, and thus changing itselectrical resistance. Alternatively or additionally, an infrared cameramay consist of a single, sensitive large pixel, such as a passiveinfrared (PIR) sensor or other single element infrared sensitivedetector.

Continuing to refer to FIG. 40 , infrared camera may use a separateaperture and/or focal plane from optical camera, and/or may beintegrated together with optical camera. There may be a plurality ofoptical cameras and/or a plurality of infrared cameras, for instancewith different angles, magnifications, and/or fields-of-view ofperspective on a subject area. Alternatively or additionally, two ormore apparatuses coordinated using a communication network, as describedin further detail below, may be combined to generate two or more imagesfrom varying perspectives to aid in multi-dimensional imaging and/oranalysis.

Still referring to FIG. 40 , imaging device 4008 may include a lightradar component. As used in this disclosure a “light radar component” isan active imaging source that transmits light toward an object or fieldof interest and detects back-scattered or reflected light, measuringtime of flight (ToF), interferometry, and/or phase of suchback-scattered and/or reflected light to compute distances to,velocities, and/or accelerations of objects at points from whichback-scatter and/or reflection occurred. In some embodiments, activelight source may include a high-intensity light source, which may befocused, collimated, and/or coherent, enabling fine placement within acoordinate system, for instance as described below, of points in a fieldof view and/or at an object of interests at which transmitted light isscattered and/or reflected; active light source may include withoutlimitation a laser, a high-intensity light-emitting diode, ahigh-intensity “super” light-emitting diode consisting of a single orplurality of lasers and/or phosphor material, super-luminescentlight-emitting diode, and/or vertical-cavity surface-emitting laser(VCSEL). A laser may include a laser diode, which may be electricallypumped; alternatively or additionally, laser may be pumped optically.Active light source may transmit light in a narrow band of wavelengths;for instance, active light source may transmit light that issubstantially monochromatic. In embodiment, light transmitted by activelight source may pass through a dichroic filter, polarizing filter,diffractive optical element, meta-material, spatial light modulator(SLM), or similar optical element, which may further narrow atransmitted wavelength range, modify the shape or pattern, modify thepolarization, modify the wavefront, or affect other properties of theactive light source. Wavelength of light may be outside the range ofvisible light; for instance, and without limitation, wavelength may bein the infrared range as described above. Light radar component mayinclude a “flash lidar” component, mechanical or non-mechanical beamsteering, light patterns, and/or computational imaging methods, such asplenoptic or other multi-aperture embodiments.

Still referring to FIG. 40 , light radar component may include one ormore optical elements for focusing, collimating, and/or transmittinglight emitted by light source. One or more optical elements may includea focal optical suite, which may bend light to converge to a real and/orvirtual focal point. Focal optical suite may be reflective, diffractive,adaptive, and/or refractive; for instance, and without limitation, focaloptical suite may include two or more lenses spaced apart, where spacingbetween lenses may be varied to modify a focal length of transmittedlight. Dispersal and/or focus of transmitted light may be controlledusing electronically focused lens assembly, where adjustment ofdistances or alignment between lenses may be electrically ormechanically actuated. Intensity or temporal composition of transmittedlight may be variable as well, where variation may be modified usingvaried voltage levels, electrical current levels, waveforms, multiplepulses, duty cycles, pulse widths, passive or active optical elements,such as Q-switches, acoustical optical tunable filters (AOTF), and/orspatial light modulators (SLM). Electrical voltage and current levels,and durations to light source may be regulated analog or digitally byoutput of a logic circuit and/or processor to a digital to analogconverter, an on/off cycle to a transistor such as a power field-effecttransistor, pulse width modulation provided natively by a processor, orthe like. In an embodiment, intensity and/or focus may default tominimally harmful settings, permitting allowing ToF ranging or the liketo determine a distance to a nearest object and/or entity in a subjectspace, after which focal length and intensity may be set as permitted bystandards of safe exposure. Alternatively or additionally, where awavelength of light source is invisible and non-ionizing, intensitylevels may be intrinsically safe across an operational range of lightsource.

With continued reference to FIG. 40 , light radar component may includeone or more optical elements may include one or more reflective,diffractive, refractive, and/or metamaterial scanning elements fordirecting a beam from light source across a space to be scanned. As anon-limiting example, one or more optical elements may make use of amirror galvanometer to direct a beam in scanning pattern. Scanning maybe performed across two dimensions, using one or more optical elementsand methods of directing individually or in combination for “beamsteering,” including but not limited to, two flat or polygonal mirrorsthat may be driven by a galvanometer, electric motors, micro-electromachined systems (MEMS) or micro-optical electro machined systems(MOEMS) microscanner devices, piezoelectric actuated devices,magnetostrictive actuated devices, liquid, polymer, or othermechanically deformable devices, fast steering mirrors (FSM), Risleyprisms, decentered macro-optical elements and micro-lens arrays, blazedgrating optical elements, MOEMS or MEMS combined with macro-opticalelements, phased arrays, electronically steered arrays, spatial lightmodulators (SLM), holographic optical elements, laser intra-cavity beamsteering, and/or metamaterial surfaces or structures. A beam mayalternatively or additionally be aimed and/or focused in three or moredimensions, for instance by using a servo-controlled lens system, whichmay be referred to without limitation as a “focus shifter,” “beamexpander,” or “z-shifter.” Intensity of emitted light may alternativelyor additionally be used. Mirrors perform a periodic motion using, forinstance, rotating polygonal mirrors and/or a freely addressable motion,as in servo-controlled galvanometer scanners. Control of scanning motionmay be effected via a rotary encoder and/or control electronicsproviding electric current to a motor or galvanometer controlling mirrorangle. Electrical current may be varied using a servo controller digitalto analog converter such as a DAC81416 as produced by Texas Instruments,Inc. of Dallas, Texas. Alternatively or additionally, the beam may beaimed and/or focused using a “non-mechanical” beam steering method, suchas spatial light modulators (SLM) by adjusting the liquid crystal matrixthat makes up the pixels of such device using digital or analog drivecontrollers to modify the angles of alignment of the liquid crystals asto make dynamic diffractive patterns to provide beam shaping and aiming.A laser's wavefront passing through the liquid crystal matrix isaffected by the calculated diffractive patterns to provide bothdeflection of the beam for aiming, and an optical function for focusingor shaping the profile of the beam.

Still referring to FIG. 40 , light radar component may include at leasta visible or infrared photodetector, which may be implemented using anysuitable visible or infrared photodetector and/or plurality of visibleor infrared photodetectors as described above. For instance, and withoutlimitation, at least a photodetector may include a detector array, suchas a detector array suitable for use in an optical or infrared camera asdescribed above. Detectors in detector array may be sensitivespecifically to a narrow band of wavelengths transmitted by lightsource, and/or may be sensitive to a range of wavelengths that includesthe band transmitted by the light source. Detectors may be designed toreact quickly to initial detection of photons, for instance through useof APDs or other highly sensitive detectors. Light radar component mayinclude one or more receptive optical elements, which may includecollimating and/or focusing mirrors and/or lenses. One or more receptiveoptical elements may include filters such as without limitationdichroic, polarization, bandpass, notch, and/or other optical filters,which may act to screen out light that is not transmitted by lightsource; this may drastically increase signal to noise ratio and mayfurther act to prevent disruption of light radar component by a directedlight deterrent as described in further detail below. Alternatively oradditionally, signal to noise ratio can be increased for the light radarcomponent by modulating the signal such that the timing or frequencyshifting of the transmitted beam is recognized by the detection circuitover the constant background ambient signal by subtracting thebackground from the signal.

In an embodiment, and further referring to FIG. 40 , light radarcomponent may perform ToF calculation, by firing pulses of light andmeasuring time required for a backscattered and/or reflected pulse toreturn. Time may be measured using an oscillator-based clock, where afaster clock signal may enable more accurate measure of the time a pulsetakes to return to detector. ToF may alternatively or additionally bemeasured using an amplitude modulated continuous wave (AMCW) technique,whereby light is emitted continuously from light source with a varyingamplitude, and a phase of returning detected light is compared to aphase of transmitted light. For instance, light source may cast amodulated illumination in a near-infrared (NIR) or short-wave infrared(SWIR) spectrum onto a scene, and then record an indirect measurement ofthe time it takes the light to travel from the light source to a portionof the scene and back using phase and/or interferometric comparison;phase comparison may, without limitation, be performed by comparing aphase of returning light to a phase of a reference beam separated fromtransmitted light using a beam splitter.

Still referring to FIG. 40 , imaging device 4044 is configured to detecta subject 4012 in a subject area 4016. As used in this disclosure an“subject” is a person, animal, object and/or substance introduced intosubject area 4016 after baseline has been established. Subject 4012 mayinclude one or more persons who are authorized to enter subject area4016 and/or one or more persons who are not authorized to enter subjectarea 4016. Subjects 4012 may be identified, tracked, imaged, recorded,analyzed, hailed and/or subjected to deterrent actions by apparatus 4004while within subject area 4016. Subject area 4016 may include a subspacewithin a facility, with boundaries defined by either a simple radiusfrom the center of subject area 4016 and/or by complex three-dimensionalboundaries. Boundaries of subject area 4016 may correspond to andcoincide with boundaries of an object or physically defined space. Forexample, and without limitation, boundaries of subject area 4016 may bethe same as aside of a physical box and/or enclosure. Boundaries ofsubject area 4016 may be defined manually via a user interface and/orautomatically by apparatus 4004. Boundaries may be imported from acomputer vision system and/or imaging device 4008. For example, andwithout limitation, imaging device 4008 may identify a specific piece ofartwork in a room and identify the boundaries as a function of the sizeof the frame of the artwork. Additionally or alternatively, boundariesmay be manually configured as a function of a graphical user interfaceand/or a speech user interface. For example, and without limitation, auser may verbally communicate the boundaries of subject area 4016 toapparatus 4004.

Still referring to FIG. 40 , subject area 4016 may include one or morebuffer zones. As used in this disclosure a “buffer zone” is a region ofspace that exists outside of the protected space. Buffer zones may besub-categorized into specific zones. For example, and without limitationa buffer zone closest to a protected space may be Zone 1, while anotherregion of the protected space may be Zone 2; specific zones may includea number n of zones, where n may be any number equal to or greaterthan 1. Buffer zones may be identified manually via a user interfaceand/or automatically by apparatus 4004, similarly to identification ofboundaries. Buffer zones may range with a degree of buffer as a functionof requirements of protected area. For example, a monument may include abuffer zone that has a minimum 4000 m requirement for each buffer zone,whereas a painting may include a buffer zone that has a minimum 2 mrequirement for each buffer zone. Additionally or alternatively, subjectarea 4016 may identify buffer zone as a function of a database ofpreviously configured and/or established protected spaces in a facility.For example, and without limitation, a database that identifies afacility with protected zones in particular areas may identify a bufferzone that allows a subject to navigate the facility without entering theprotected zones without authorization.

Still referring to FIG. 40 , subject area 4016 may automatically adjustboundaries as a function of changes in a facility and/or changes inbuffer zones. For example, and without limitation, imaging device 4008may implement a lidar device that determines that an object in subjectarea 4016 has moved, wherein that object was required to be protected.Apparatus 4004 may adjust subject area 4016 to ensure that objectremains in the protected zone with the required buffer zone distances.As a further non-limiting example apparatus 4004 may adjust subject area4004 if an object is removed from the subject area that no longerrequires protection, such as a wristwatch is removed and worn by anindividual, wherein the wristwatch is allowed to exit the subject area,reducing the subject area for a specific period of time.

Still referring to FIG. 40 , apparatus 4004 includes a threat responsecomponent 4020. As used in this disclosure a “threat response component”is a component and/or device configured to generate a non-lethaldeterrent. A deterrent may include any physical or psychologicalinteraction that discourages and/or stops an object and/or entity fromperforming a behavior contrary to objectives of apparatus 4004. Threatresponse component 4020 includes an audio deterrent 4024, for instanceas defined above. Audio deterrent 4024 may output verbal messages suchas instructions and/or recommendations. Audio deterrent 4024 may outputwarning sounds and/or louder noises that may act as a direct deterrent,such as a noise and/or sound that is capable of producing a nociceptiveand/or disruptive response in subject 4012. Audio deterrent 4024 maybroadcast the message, signal and/or waveform to a particular individualand/or a group of individuals. Audio deterrent 4024 may output a verbalmessage relating to a particular subject's behavior and/or appearance.Audio deterrent 4024 may output a verbal message relating to a group ofsubject's behavior and/or appearance. Audio deterrent 4024 may include ageneration, transmission and/or detection of vibrational energy. As anon-limiting example, vibrational energy may include any energy that maybe transmitted through any available substance and/or medium, includingbut not limited to air, water, building materials, body tissues, and thelike thereof. Audio deterrent 4024 may include a generation,transmission, and/or detection of ultrasound energy. Audio deterrent4024 may include a generation, transmission, and/or detection ofinfrasound energies.

Still referring to FIG. 40 , audio deterrent 4024 may employ an activepolicy of incremental intervention to provide effective interventionresults. As used in this disclosure an “incremental intervention” is anintervention that is dynamically adjusted to maximize the likelihood ofa successful resolution of a threat, with a minimal intervention. As anon-limiting example, an incremental intervention may includedetermining that a verbal output of “Cease and Desist!” would be aseffective as an ultrasonic emission for a behavior of encroachment.Incremental intervention may escalate in intensity, quality, duration,location, and the like thereof as a result of the threat failing to beneutralized. Additionally or alternatively, audio deterrent 4024 mayoutput a ding and/or chirp sound to at least alert surroundingindividuals of an event that is developing. For example, and withoutlimitation, a subject area of a particular room in a house may beemployed to prevent children from entering, wherein if a child doesenter a chirp sound is emitted to notify adults of the situation and beaware of the situation.

In an embodiment and still referring to FIG. 40 , audio deterrent 4024may include one or more multiple-focused energy weapons. As used in thisdisclosure a “multiple-focused energy weapon” is an energy and/or audiosignal that may be directed at a target or location from one or moresource locations. Multiple-focused energy weapons may include deployinga coordinated multi-source audio intervention. For example,multiple-focused energy weapons may enable a “triangulation” type ofeffect of audio signals and/or outputs similar to the manner in which a“gamma-knife” medical system focuses gamma energy at a particular spotin the body; there may be limited impact of any one audio signal and/oroutput coming from a single direction, but the cumulative impact of allthe audio signals and/or outputs coinciding at one location, or for onetarget, may be extreme. Multiple-focused energy weapons may alsoincrease the resistance of apparatus 4004 to defensive strategies on thepart of a threating subject 4012. For example, and without limitation itmay be less possible for subject 4012 to simply turn away from thesource of the audio signal and/or output and/or hide behind furniture insubject area 4016 as the audio signal and/or output may be coincidingfrom more than one source and/or origination.

Still referring to FIG. 40 , audio deterrent 4024 may include a class ofaudio interventions. As used in this disclosure a “class” is a groupand/or style of audio signals. As a non-limiting example, a class ofaudio interventions may include non-speech and spoken audio classes.Non-speech audio classes may include organic signals, engineeredsignals, algorithmic signals, composed signals, random signals, and/orany other type of non-human speech signals. Non-speech audio classes maybe simple and/or complex with one or more frequency components.Frequency components may be audible, sub-audible, and/or super-audibleperceptible signals. Non-speech audio classes may include noises such aswhite noise, pink noise, brown noise, and the like thereof. Non-speechaudio classes may include a single pulse and/or burst of signal as wellas a pattern of noises with any tempo, pattern, rhythm, and/orrepetition. Spoken audio classes may include natural and/or artificiallygenerated speech such as words, phrases, sentences, and the likethereof. Spoken audio classes may include grunts, yells, coughs,sneezes, and/or other bodily noises. Spoken audio classes may includeboth male, female, and/or any other sex and/or gender audio signals.Spoken audio classes may relate to a particular identify of anindividual such as a mother, a father, and/or any other identifiableinformation in the spoken audio.

In an embodiment and still referring to FIG. 40 , audio deterrent 4024may include a directional audio deterrent, for instance and withoutlimitation as described above.

Still referring to FIG. 40 , directed sound source may include one ormore parabolic reflectors. As used in this disclosure a “parabolicreflector” is one or more speaker drivers mounted at the focal point ofa parabola, toward the parabolic surface. Parabolic reflectors may emita sound that may be bounced off of a parabolic dish and leave the dishfocused in plane waves. Parabolic reflectors may have a diameter twicethat of the wavelength of the lowest desired frequency. For example, andwithout limitation, parabolic reflectors may obtain directional controlof a 20 Hz signal when utilizing a dish that is more than 15.24 m and/or50 ft wide. Additionally or alternatively, directed sound source mayinclude a sonic emitter. As used in this disclosure a “sonic emitter” isa directed sound source that emits extremely high-power sound waves.Sonic emitters may disrupt and/or destroy the eardrums of subject torender the subject incapacitated. Sonic emitters may cause severe painand/or disorientation to subjects. As a non-limiting example, sonicemitters may produce frequencies that cause vibrations of the eyeballsthat disorient and/or distort the vision of the subject. As a furthernon-limiting example, sonic emitters may include, sonic bullets, sonicgrenades, sonic mines, sonic cannons, and the like thereof.

Still referring to FIG. 40 , audio deterrent 4024 may include acomposite wave front. As used in this disclosure a “composite wavefront” is a set of points that has the same phase of the sinusoid suchthat the set of points are affected in the same way by the givensinusoid at a given time. Compositive wave front may be generated as afunction of a wave field synthesis in three dimensions, wherein threedimensions are composed of the x, y, and z directions. As used in thisdisclosure a “wave field synthesis” is the synthesis of a composite wavefront as a function of a superposition of numerous smaller wavefronts.In an embodiment, and without limitation, wave field synthesis maygenerate composite wave front as a function of overlapping wavefrontsoriginating from actual sources at other positions. As a non-limitingexample, a wave front may be generated due to loudspeaker arrays thatare arranged in a line, plane, and/or circle around a listener that atleast overlap wave fronts at a specified points, and/or location. Wavefield synthesis may generate composite wave front by emitted numeroussmaller wavefronts using measured delays to produce the desiredcompositive wave front shape.

Still referring to FIG. 40 , wave front synthesis may generate compositewave front as a function of a virtual source. As used in this disclosurea “virtual source” is a wave front that results in a source at aspecified location. Virtual source may include a plane wave source. Asused herein, a “plane wave source” is a source positioned at an infinitedistance beyond the array of wave fronts. For example, and withoutlimitation plane wave source may emit wave fronts that are driven at alinear delay based on the angle of incidence of the plane wave relativeto the array. Plane wave source may propagate directly perpendicular tothe array, wherein there is no delay, and the wave fronts are drivenwith the source signal uniformly, wherein the plane wave source may besimulated according to

$\vartheta_{NH} = {{\sin^{- 1}\frac{c}{c_{s}}} = {\sin^{- 1}\frac{\lambda}{\lambda_{s}}}}$

where ∂_(NH) is the beam direction corresponding to the direction ofplane wave propagation, A indicates the wavelength of the signal in thedirection of propagation, A, indicates the wavelength of the signalalong the secondary source array, and c, is the “sweeping speed” thatdescribes how fast the signal is shifted across the line array.Additionally or alternatively, virtual source may include a sphericalsource. As used in this disclosure a “spherical source” is a source thatemits wavefronts as a species of spherical waves. As a non-limitingexample spherical source may encompass a crowd of insurgents to at leastincapacitate the crowd using audio deterrent 4024. Virtual source mayinclude a focused source. As used in this disclosure a “focused source”is a source that comprises a location between audio deterrent 4024 andan individual, and/or group of individuals. In an embodiment, andwithout limitation, a focused source may include a composite wave frontthat originates at a location differing from subject 4012 and audiodeterrent 4024. As a non-limiting example, focused source may include apoint at which composite wave front occurs in an area, such that anindividual perceives that sound to be originating at that location. Forexample, and without limitation, a virtual source may be generated 20 mdirectly to the right of an individual to sound as though lawenforcement have fired a bullet from a firearm. As a furthernon-limiting example, a virtual source may be generated 200 m in the skyto sound as though a helicopter is hovering above an individual.

In an embodiment, and without limitation, apparatus 4004 may emit afirst voice command to subject 4012 as a function of a composite wavefront that at least overlaps at a location that is audible to subject4012. For example, and without limitation, a composite wavefront of averbal command to cease and desist may be transmitted such that thecomposite wavefront originates at a location 1 m from subject 4012within a crowd. Audio deterrent 4024 may emit a spherical sourcesurrounding subject 4012 that results in wave fronts that mitigateand/or prevent surrounding crowd noises from interacting with subject4012. In an embodiment, and without limitation, a spherical source mayprevent surrounding individuals from talking over and/or masking noiseemitted from audio deterrent 4024. Audio deterrent 4024 may emit a highSound Pressure Level (SPL) to subject 4012 as a function of a sphericalfocused wavefront. As used in this disclosure a “sound pressure level”is an acoustic pressure level comprising a logarithmic measure of theeffective pressure of a sound relative to a reference value. As anon-limiting example, audio deterrent 4024 may emit a high SPL, 160 dB,noise for a subject that is wearing hearing protection, wherein audiodeterrent 4024 may emit a regular SPL, 140 dB, noise for a subject thatis not wearing hearing protection.

Still referring to FIG. 40 , audio deterrent 4024 may couple and/or syncaudio deterrent 4024 with imaging device 4008 to create fear in thesubject and/or the surrounding crowd. In an embodiment, audio deterrent4024 may emit holographic effects. As used in this disclosure“holographic effects” are audio effects that result in spatialreconstruction of direct and reflected wave fields with desiredwavefront properties at each moment of time. Holographic effects may bereconstructed such that sound fields from holographic effects areindistinguishable from natural sounds. For example, and withoutlimitation, holographic effects may include helicopter sounds flyingoverhead, gun fire by law enforcement, smoke grenades by lawenforcement, hand grenades by military and/or law enforcement, screamsof individuals and/or crowds, and the like thereof. Holographic effectsmay include one or more perceptions of distance. As used in thisdisclosure a “perception of distance” is a measurable distance at whichthe individual perceives the audio signal is originating from.Perception of distance may be developed as a function of loudness,interaural difference, direct-to-reverberant energy ratio, initial timedelay group, frequency spectrum, reflection pattern, motion parallax,and the like thereof. For example, and without limitation a source lessthat is 0.5 m away will produce different interaural cues than a sourceat the same direction 1 m away because the distance between the earsbecomes significant. As a further example, a perception of distancealters as a function of the direct reverberant energy decreasing due toa source moving away from an individual. As a further non-limitingexample, perception of distance may be altered as a function of thedirect source sound wave and the first reflection wave at the listenersposition, wherein the delay between the direct and reflect sound isgreater when the source is neared to the individual. As a furthernon-limiting example, perception of distance may be altered as afunction of air dampening, wherein air dampening is stronger at higherfrequencies. As a further non-limiting example, perception of distancemay be altered due to directional qualities of the reflection patternsof the source. As a further non-limiting example, perception of distancemay be altered due to the corresponding change of perspective movementswherein if one moves 2 m towards a first source perception may bealtered in a different manner than when compared to moving 2 m towards asecond source.

Still referring to FIG. 40 , audio deterrent 4024 is configured toperform a deterrent action. As used in this disclosure a “deterrentaction” is an action conducted by apparatus 4004 that relates to abehavior and/or audio output by subject 4012, wherein the actionconducted mitigates and/or eliminates the behavior and/or audio outputby subject 4012. Deterrent action may include an audio deterrent actionas a function of the behavior and/or audio output by subject 4012. Asused in this disclosure an “audio deterrent action” is a deterrentaction consisting of an audio signal, wave, and/or message. As anon-limiting example an audio deterrent action may include emitting asound and/or signal that invokes a response by subject 4012. Forexample, and without limitation audio deterrent action may includeemitting a voice command for subject 4012 to follow. As a furthernon-limiting example deterrent action may include emitting a noiseand/or signal that renders subject 4012 incapacitated. Deterrent actionmay include a first deterrent action 4028 on subject 4012. As used inthis disclosure a “first deterrent action” is a first action conductedby audio deterrent 4024 that relates to a first threat level 4032. Asused in this disclosure a “first threat level” is a first level that isidentified as a function of an object's and/or entities threateningbehaviors. As a non-limiting example a first threat level may includeshouting, condescending tones, speaking loudly, and the like thereof. Asa further non-limiting example, a first threat level may includeentering a restricted area, and/or trespassing into a secured area. As afurther non-limiting example a first threat level may include asubject's presence in a subject area, wherein the subject is notauthorized to be in the subject area. As a further non-limiting examplea first threat level may include behavior representing potential issues,such as loitering, and/or prowling. As a further non-limiting example, afirst threat level may include noncompliance with a first audio and/orvisual instruction provided by apparatus 4004. As a further non-limitingexample, a first threat level may include erratic behavior and/ormovement toward an object to be protected and/or secured. As a furthernon-limiting example, a first threat level may include obscene gesturesand/or behaviors. Audio deterrent is configured to perform a seconddeterrent action 4036 on subject 4012. As used in this disclosure a“second deterrent action” is an action conducted by audio deterrent 4024that relates to a second threat level 4040. As used in this disclosure a“second threat level” is a second level that is identified as a functionof an object's and/or entities threatening behaviors. As a non-limitingexample, second threat level may include violence, abuse, physicalaltercation, and the like thereof. First deterrent action 4028 isdistinct from second deterrent action 4036. For example, and withoutlimitation, first deterrent action 4028 may include a sound emitted toalter attention of the subject, wherein second deterrent action 4036 mayinclude incapacitating the subject as a function of an ultrasonic wave.As a further non-limiting example, first deterrent action 4028 mayinclude emitting a sentence to at least communicate with the subject,wherein second deterrent action 4036 may include emitting a loud and/oruncomfortable audio signal.

Still referring to FIG. 40 , apparatus 4004 includes a processor 4044communicatively connected to imaging device 4008 and threat responsecomponent 4020. As used in this disclosure a “processor” is a machineand/or computing device that receives inputs and generates outputs.Processor 4044 is configured to identify subject 4012 as a function ofthe detection of the subject. As a non-limiting example, processor mayreceive inputs from imaging device 4008 representing a 23-29-year-oldmale, wearing a green uniform, associated with janitorial services,wherein the individual may be identified as subject 4012. Additionallyor alternatively, processor 4044 may be configured to determine apersonal identity of subject 4012 as a function of the detection of thesubject. As a non-limiting example, processor 4044 may determine thename Andrea for subject 4012 as a function of the detection of thesubject. Moreover, processor 4044 may determine an authorization levelof subject 4012 as a function of the personal identity. As used in thisdisclosure an “authorization level” is the rights and/or permissions asubject has with regards to actions, behaviors, and/or subject areas.For example, and without limitation, personal identity may be determinedto be John Whitworth, wherein an authorization level of 5 may beidentified for the personal identity. As a further non-limiting example,personal identity may be determined to be Lucy Steele, wherein anauthorization level of “Top Secret” may be identified for the personalidentity.

Still referring to FIG. 40 , processor 4044 is configured to determine athreat level 4048, of first threat level 4032 and second threat level4040 associated with subject 4012. As used in this disclosure a “threatlevel” is the range of threats that may or may not be exhibited bysubject 4012. As a non-limiting example, threat level 4048 may includefirst threat level 4032, second threat level 4040, and/or n-threatlevels, wherein n-threat levels are any subsequent threatening levelsthat may or may not be exhibited by subject 4012. For example, andwithout limitation, threat level 4048 may include a first threat ofobnoxious behavior, wherein the range may extend to property damageand/or uncontrollable rage. Threat level 4048 may be determined as afunction of determining a time of day. As used in this disclosure a“time of day” is a temporal element associated with the current day thatapparatus 4004 is operating. As a non-limiting example threat level 4048may include a range of threat levels as a function of a time of dayassociated with 08:00 AM, wherein a different range of threat levels maybe established for similar and/or the same behaviors as a function of atime of day associated with 09:00 PM.

Still referring to FIG. 40 , threat level 4048 may be determined as afunction of processor 4044 measuring a distance to subject 4012. As anon-limiting example, a subject that is 2 m and/or 6.56 ft fromapparatus 4004 may result in a range associated with a first threatlevel of shouting, wherein the range includes violence and/or damage toapparatus 4004. As a further non-limiting example, a subject that is 17m and/or 55.77 ft from apparatus 4004 may result in a range associatedwith a first threat level of obnoxious behavior, wherein the rangeincludes verbal abuse. Processor 4044 may measure a distance from lightradar component to a point from which light is scattered; this may beused, without limitation, to detect distance to an object. Distance maybe computed using a single reading of ToF, by averaging two or more ToFreadings, and/or measuring multiple returns to reduce false readingsfrom clutter. ToF may be used to detect edges of objects, a portion ofanatomy, an object held, or the like. For instance, and withoutlimitation, an edge may be detected by comparison of ToF at detectedpoints to nearby and/or adjacent ToF readings, where a border separatinga region of relatively smaller ToF readings from a region of relativelymore distant ToF readings may identify an edge. As a non-limitingexample, such a border may define an outline of a person with a wall orother object behind the person. ToF may be used to generate an image,for instance by repeatedly capturing readings of ToF to differentportions of an object; a three-dimensional surface contour of theobject, such as facial features, details of an object a person isholding, or the like, may be rendered using the ToF data. ToFmeasurements may be processed to generate a depth map or point cloud,defined for the purposes of this disclosure as a set of Z-coordinatevalues for every pixel of the image, which may be measured in units ofmillimeters, micrometers, or the like. Depth map data may be combinedwith other imaging data; for instance, intensity or phase values ofpixels in an infrared reading may be measured as proportional to anamount of light returned from a scene.

Still referring to FIG. 40 , threat level 4048 may be determined as afunction of processor 4044 detecting a behavior of subject 4012. As usedin this disclosure a “behavior” is an action and mannerism performed byan individual, organism, system, or artificial entities in conjunctionwith themselves or their environment, which includes the other systemsor organisms around as well as the physical environment. Behavior may bedetermined as a function of a behavior descriptor. As used in thisdisclosure a “behavior descriptor” is information that at least relatesto a user's intended behavior decision. Behavior descriptor may include,without limitation, micro expression, macroexpressions, language, tone,word selection, physiological actions, and the like thereof. As anon-limiting example, behavior descriptor may relate a microexpressionof a nose wrinkled with a negative behavior of disgust. Behavior is thendetermined as a function of behavior descriptor. Behavior may bedetermined as a function of a behavior machine-learning model. As usedin this disclosure a “behavior machine-learning model” is amachine-learning model that uses training data and/or training set togenerate an algorithm that will be performed by apparatus 4004 and/or aremote device to produce outputs given data provided as inputs; this isin contrast to a non-machine-learning software program where thecommands to be executed are determined in advance by a user and writtenin a programming language. As used in this disclosure a “remote device”is an external computing device and/or processor to apparatus 4004.Behavior machine-learning model may consist of any supervised,unsupervised, or reinforcement machine-learning model that apparatus4004 may or may not use in the determination of the behavior. Behaviormachine-learning model may include, without limitation machine-learningmodels such as simple linear regression, multiple linear regression,polynomial regression, support vector regression, ridge regression,lasso regression, elasticnet regression, decision tree regression,random forest regression, logistic regression, logistic classification,K-nearest neighbors, support vector machines, kernel support vectormachines, naive bayes, decision tree classification, random forestclassification, K-means clustering, hierarchical clustering,dimensionality reduction, principal component analysis, lineardiscriminant analysis, kernel principal component analysis, Q-learning,State Action Reward State Action (SARSA), Deep-Q network, Markovdecision processes, Deep Deterministic Policy Gradient (DDPG), or thelike thereof. Behavior machine-learning model may be trained as afunction of a behavior training set. As used in this disclosure“behavior training set” is a training set that correlates an input to atleast a behavior descriptor, wherein input comprises any datum and/orinformation that the imaging device and/or microphone collects, stores,and/or transmits, wherein a microphone is an instrument that collectsaudio information as described below in detail, in reference to FIG. 41.

Still referring to FIG. 40 , remote device 4036 may providemodifications to the behavior machine-learning model. For example, andwithout limitation, a modification may be comprised of a firmwareupdate, a software update, a behavior machine-learning correction, andthe like thereof. As a non-limiting example a software update mayincorporate a new behavior machine-learning model that relates to inputof a plurality of inputs to a modified behavior descriptor. As a furthernon-limiting example a remote device may transmit a modified behaviormachine-learning model, wherein the modified behavior machine-learningmodel may relate new behavior descriptors to previously identifiedinputs of a plurality of inputs. Additionally or alternatively, behaviormachine-learning model may be transmitted to remote device, whereinremote device may update the behavior training data and transmit anupdated behavior machine-learning model back to apparatus 4004. Theupdated behavior machine-learning model may be transmitted by remotedevice and received by apparatus 4004 as a software update, firmwareupdate, or corrected behavior machine-learning model. Additionally oralternatively, remote device may include the behavior machine-learningmodel, wherein apparatus 4004 transmits a signal, bit, datum, orparameter to the remote device and receives the outputted behavior fromthe behavior machine-learning model on remote device 4036.

Still referring to FIG. 40 , behavior machine-learning model may begenerated as a function of a behavior classifier. A “behaviorclassifier,” as used in this disclosure is a machine-learning model,such as a mathematical model, neural net, or program generated by amachine-learning algorithm known as a “classification algorithm,” asdescribed in further detail below, that sorts inputs of behaviorinformation into categories or bins of data, outputting the categoriesor bins of data and/or labels associated therewith. Behavior classifiermay be configured to output at least a datum that labels or otherwiseidentifies a set of behaviors that are clustered together, found to beclose under a distance metric as described below, or the like. Apparatus4004 and/or another device may generate behavior classifier using aclassification algorithm, defined as a process whereby apparatus 4004derives a classifier from training data. Classification may includemapping behaviors to a semantic meanings and/or tones. For example, andwithout limitation, classification may include mapping behaviors ofvulgar language to semantic meanings and/or tones of aggression and/orviolence. Classification may be performed using, without limitation,linear classifiers such as without limitation logistic regression and/ornaive Bayes classifiers, nearest neighbor classifiers such as k-nearestneighbors classifiers, support vector machines, least squares supportvector machines, fisher's linear discriminant, quadratic classifiers,decision trees, boosted trees, random forest classifiers, learningvector quantization, and/or neural network-based classifiers.

Still referring to FIG. 40 , apparatus 4004 may be configured togenerate a classifier using a Naïve Bayes classification algorithm.Naïve Bayes classification algorithm generates classifiers by assigningclass labels to problem instances, represented as vectors of elementvalues. Class labels are drawn from a finite set. Naïve Bayesclassification algorithm may include generating a family of algorithmsthat assume that the value of a particular element is independent of thevalue of any other element, given a class variable. Naïve Bayesclassification algorithm may be based on Bayes Theorem expressed asP(A/B)=P(B/A) P(A)÷P(B), where P(A/B) is the probability of hypothesis Agiven data B also known as posterior probability; P(B/A) is theprobability of data B given that the hypothesis A was true; P(A) is theprobability of hypothesis A being true regardless of data also known asprior probability of A; and P(B) is the probability of the dataregardless of the hypothesis. A naive Bayes algorithm may be generatedby first transforming training data into a frequency table. Apparatus4004 may then calculate a likelihood table by calculating probabilitiesof different data entries and classification labels. Apparatus 4004 mayutilize a naive Bayes equation to calculate a posterior probability foreach class. A class containing the highest posterior probability is theoutcome of prediction. Naïve Bayes classification algorithm may includea gaussian model that follows a normal distribution. Naïve Bayesclassification algorithm may include a multinomial model that is usedfor discrete counts. Naïve Bayes classification algorithm may include aBernoulli model that may be utilized when vectors are binary.

With continued reference to FIG. 40 , apparatus 4004 may be configuredto generate a classifier using a K-nearest neighbors (KNN) algorithm. A“K-nearest neighbors algorithm” as used in this disclosure, includes aclassification method that utilizes feature similarity to analyze howclosely out-of-sample-features resemble training data to classify inputdata to one or more clusters and/or categories of features asrepresented in training data; this may be performed by representing bothtraining data and input data in vector forms, and using one or moremeasures of vector similarity to identify classifications withintraining data, and to determine a classification of input data.K-nearest neighbors algorithm may include specifying a K-value, or anumber directing the classifier to select the k most similar entriestraining data to a given sample, determining the most common classifierof the entries in the database, and classifying the known sample; thismay be performed recursively and/or iteratively to generate a classifierthat may be used to classify input data as further samples. Forinstance, an initial set of samples may be performed to cover an initialheuristic and/or “first guess” at an output and/or relationship, whichmay be seeded, without limitation, using expert input received accordingto any process as described herein. As a non-limiting example, aninitial heuristic may include a ranking of associations between inputsand elements of training data. Heuristic may include selecting somenumber of highest-ranking associations and/or training data elements.

With continued reference to FIG. 40 , generating k-nearest neighborsalgorithm may generate a first vector output containing a data entrycluster, generating a second vector output containing an input data, andcalculate the distance between the first vector output and the secondvector output using any suitable norm such as cosine similarity,Euclidean distance measurement, or the like. Each vector output may berepresented, without limitation, as an n-tuple of values, where n is atleast two values. Each value of n-tuple of values may represent ameasurement or other quantitative value associated with a given categoryof data, or attribute, examples of which are provided in further detailbelow; a vector may be represented, without limitation, in n-dimensionalspace using an axis per category of value represented in n-tuple ofvalues, such that a vector has a geometric direction characterizing therelative quantities of attributes in the n-tuple as compared to eachother. Two vectors may be considered equivalent where their directions,and/or the relative quantities of values within each vector as comparedto each other, are the same; thus, as a non-limiting example, a vectorrepresented as [5, 10, 15] may be treated as equivalent, for purposes ofthis disclosure, as a vector represented as [1, 2, 3]. Vectors may bemore similar where their directions are more similar, and more differentwhere their directions are more divergent; however, vector similaritymay alternatively or additionally be determined using averages ofsimilarities between like attributes, or any other measure of similaritysuitable for any n-tuple of values, or aggregation of numericalsimilarity measures for the purposes of loss functions as described infurther detail below. Any vectors as described herein may be scaled,such that each vector represents each attribute along an equivalentscale of values. Each vector may be “normalized,” or divided by a“length” attribute, such as a length attribute l as derived using aPythagorean norm: l=√{square root over (Σ_(i=0) ^(n)a_(i) ²)}, wherea_(i) is attribute number i of the vector. Scaling and/or normalizationmay function to make vector comparison independent of absolutequantities of attributes, while preserving any dependency on similarityof attributes; this may, for instance, be advantageous where casesrepresented in training data are represented by different quantities ofsamples, which may result in proportionally equivalent vectors withdivergent values.

Still referring to FIG. 40 , threat level 4048 may be determined as afunction of processor 4044 identifying an object in possession ofsubject 4012. As used in this disclosure an “object” is one or moreinanimate compositions of matter that may or may not be utilized inconducting a behavior. As a non-limiting example, an object inpossession of subject 4012 may include one or more cell phones,flashlights, shoes, pens, neckties, wallets, keys, water bottles, andthe like thereof. As a further non-limiting example, an object inpossession of subject 4012 may include one or more knives, firearms,axes, sharp objects, crowbars, tire irons, and the like thereof. Objectsin possession may be associated with one or more behaviors as a functionof a tool classifier. As used in this disclosure a “tool classifier” isa machine-learning model, such as a mathematical model, neural net, orprogram generated by a tool machine-learning algorithm known as a “toolclassification algorithm,” as described in further detail below, thatsorts inputs of tool information into categories or bins of data,outputting the categories or bins of data and/or labels associatedtherewith. As a non-limiting example, tool classifier may identify athreat level for a tool associated with an axe, wherein a differentthreat level may be identified for a tool associated with a pen.

Still referring to FIG. 40 , processor 4044 may be configured to detecta speech of the subject. As used in this disclosure “speech” is one ormore audio signals and/or sound waves produced by subject 4012 thatrelates to a language and/or form of communication. As a non-limitingexample, speech of a subject may include one or more communicative wordsand/or phrases from a plurality of languages such as English, German,Japanese, Chinese, Polish, Italian, Swedish, and the like thereof.Processor 4044 may detect language as a function of determining asemantic tone associated with the speech of the subject. As anon-limiting example, a semantic tone may include a unique pitch and/orkey that a word and/or phrase is spoken in that alters and/ordistinguishes lexical and/or grammatical meaning.

Still referring to FIG. 40 , process 4044 may detect speech as afunction of a language processing module. Language processing module mayinclude any hardware and/or software module. Language processing modulemay be configured to extract, from the one or more documents, one ormore words. One or more words may include, without limitation, stringsof one or more characters, including without limitation any sequence orsequences of letters, numbers, punctuation, diacritic marks, engineeringsymbols, geometric dimensioning and tolerancing (GD&T) symbols, chemicalsymbols and formulas, spaces, whitespace, and other symbols, includingany symbols usable as textual data as described above. Textual data maybe parsed into tokens, which may include a simple word (sequence ofletters separated by whitespace) or more generally a sequence ofcharacters as described previously. The term “token,” as used herein,refers to any smaller, individual groupings of text from a larger sourceof text; tokens may be broken up by word, pair of words, sentence, orother delimitation. These tokens may in turn be parsed in various ways.Textual data may be parsed into words or sequences of words, which maybe considered words as well. Textual data may be parsed into “n-grams”,where all sequences of n consecutive characters are considered. Any orall possible sequences of tokens or words may be stored as “chains”, forexample for use as a Markov chain or Hidden Markov Model.

Still referring to FIG. 40 , language processing module may operate toproduce a language processing model. Language processing model mayinclude a program automatically generated by computing device and/orlanguage processing module to produce associations between one or morewords extracted from at least a language and detect associations,including without limitation mathematical associations, between suchwords. Associations between language elements, where language elementsinclude for purposes herein extracted words, relationships of suchcategories to other such term may include, without limitation,mathematical associations, including without limitation statisticalcorrelations between any language element and any other language elementand/or language elements. Statistical correlations and/or mathematicalassociations may include probabilistic formulas or relationshipsindicating, for instance, a likelihood that a given extracted wordindicates a given category of semantic meaning. As a further example,statistical correlations and/or mathematical associations may includeprobabilistic formulas or relationships indicating a positive and/ornegative association between at least an extracted word and/or a givensemantic meaning; positive or negative indication may include anindication that a given document is or is not indicating a categorysemantic meaning. Whether a phrase, sentence, word, or other textualelement in a document or corpus of documents constitutes a positive ornegative indicator may be determined, in an embodiment, by mathematicalassociations between detected words, comparisons to phrases and/or wordsindicating positive and/or negative indicators that are stored in memoryat computing device, or the like.

Still referring to FIG. 40 , language processing module and/ordiagnostic engine may generate the language processing model by anysuitable method, including without limitation a natural languageprocessing classification algorithm; language processing model mayinclude a natural language process classification model that enumeratesand/or derives statistical relationships between input term and outputterms. Algorithm to generate language processing model may include astochastic gradient descent algorithm, which may include a method thatiteratively optimizes an objective function, such as an objectivefunction representing a statistical estimation of relationships betweenterms, including relationships between input terms and output terms, inthe form of a sum of relationships to be estimated. In an alternative oradditional approach, sequential tokens may be modeled as chains, servingas the observations in a Hidden Markov Model (HMM). HMMs as used hereinare statistical models with inference algorithms that that may beapplied to the models. In such models, a hidden state to be estimatedmay include an association between an extracted word category ofphysiological data. There may be a finite number of categories to whichan extracted word may pertain; an HMM inference algorithm, such as theforward-backward algorithm or the Viterbi algorithm, may be used toestimate the most likely discrete state given a word or sequence ofwords. Language processing module may combine two or more approaches.For instance, and without limitation, machine-learning program may use acombination of Naïve-Bayes (NB), Stochastic Gradient Descent (SGD), andparameter grid-searching classification techniques; the result mayinclude a classification algorithm that returns ranked associations.

Continuing to refer to FIG. 40 , generating language processing modelmay include generating a vector space, which may be a collection ofvectors, defined as a set of mathematical objects that can be addedtogether under an operation of addition following properties ofassociativity, commutativity, existence of an identity element, andexistence of an inverse element for each vector, and can be multipliedby scalar values under an operation of scalar multiplication compatiblewith field multiplication, and that has an identity element isdistributive with respect to vector addition, and is distributive withrespect to field addition. Each vector in an n-dimensional vector spacemay be represented by an n-tuple of numerical values. Each uniqueextracted word and/or language element as described above may berepresented by a vector of the vector space. In an embodiment, eachunique extracted and/or other language element may be represented by adimension of vector space; as a non-limiting example, each element of avector may include a number representing an enumeration ofco-occurrences of the word and/or language element represented by thevector with another word and/or language element. Vectors may benormalized, scaled according to relative frequencies of appearanceand/or file sizes. In an embodiment associating language elements to oneanother as described above may include computing a degree of vectorsimilarity between a vector representing each language element and avector representing another language element; vector similarity may bemeasured according to any norm for proximity and/or similarity of twovectors, including without limitation cosine similarity, which measuresthe similarity of two vectors by evaluating the cosine of the anglebetween the vectors, which can be computed using a dot product of thetwo vectors divided by the lengths of the two vectors. Degree ofsimilarity may include any other geometric measure of distance betweenvectors.

Still referring to FIG. 40 , processor 4044 is configured to commandaudio deterrent 4024 to perform an action of first deterrent action 4028and second deterrent action 4036 as a function of the determined threatlevel. In an embodiment and without limitation first processor 4044 maycommand audio deterrent 4024 to emit first deterrent action 4028 to emitan audio signal associated with a warning, such as “alert”, “caution”and/or “prevent”. Processor 4044 may command audio deterrent 4024 todeploy audio alerting and/or notifying of threats, events, and/or statusin subject area 4016. For example, audio or sounds that are deployed asan alert may depend on the attributes of the event, threat, or statusthat is being addressed. In one embodiment, the specific type of threatmay determine or influence the alert. For example, one type of audio(e.g., a simple “chime”) may be deployed when a person enters thebuilding or buffer zone, whereas a different type of audio (e.g., a“ding-ding-ding”) may be deployed when a specific spoken phrase isidentified. In a further embodiment, the location of the threat maydetermine or influence the alert. As a non-limiting example, an alertaudio signal may include a single 400 Hz chirp with moderate rise timeof 50 ms and a duration of 200 ms, played at 75 dB SPL. As a furthernon-limiting example, an alert may include a pattern of sounds composedby playing a pre-recorded buzz and/or “raspberry” sound three times inrapid succession. As a further non-limiting example, when an individualenters a particular door of the facility, the alert audio may be playednear that door. In another embodiment, information about the facility,its status, occupants, or activities may influence the alert sound. Forexample, if the system determines that the principal of a school islocated in a particular office within the facility, then when anindividual enters the building, the resulting alert may be played in theroom in which the principal is located.

In an embodiment, and still referring to FIG. 40 , processor 4044 mayinclude a language processing component that constructs a word and/orphrase that processor 4044 may command audio deterrent 4024 to perform.Language processing component may receive inputs from imaging device4008 and/or microphones, wherein microphones receive audio signalsand/or sound waves as described below, in reference to FIG. 41 . Inputsand/or outputs may be exchanged using audio signals, sound waves, and/oroptical signals, including without limitation any datum outputs from animage classifier, tool classifier and/or behavior classifier. An “imageclassifier,” as used in this disclosure is a machine-learning model,such as a mathematical model, neural net, or program generated by amachine-learning algorithm known as a “classification algorithm,” asdescribed in further detail below, that sorts inputs of imageinformation into categories or bins of data, outputting the categoriesor bins of data and/or labels associated therewith. Image classifier maybe configured to output at least a datum that labels or otherwiseidentifies a set of images that are clustered together, found to beclose under a distance metric as described below, or the like. Apparatus4004 and/or another device may generate image classifier using aclassification algorithm, defined as a process whereby apparatus 4004derives a classifier from training data. Classification may be performedusing, without limitation, linear classifiers such as without limitationlogistic regression and/or naive Bayes classifiers, nearest neighborclassifiers such as k-nearest neighbors classifiers, support vectormachines, least squares support vector machines, fisher's lineardiscriminant, quadratic classifiers, decision trees, boosted trees,random forest classifiers, learning vector quantization, and/or neuralnetwork-based classifiers. Classification may include mapping images toa semantic meanings and/or tones. For example, and without limitation,classification may include mapping images of blatant trespassing tosemantic meanings and/or tones of disrespect. Persons skilled in theart, up reviewing the entirety of this disclosure, will be aware of amultiplicity of methods that may be utilized for language processingcomponent to construct words and/or phrases as described herein.Language processing component may generate chat bots that containmeaningful messages and advice to facilitate behavioral changes.Language processing component may construct words and/or phrases thatprovide instructions and/or guidance to subject 4012 such asinstructions for subject 118 to alter their actions, such astrespassing, obscenities, and the like thereof. Language processingcomponent may construct words and/or phrases that may be used tocommunicate with subject 4012 such that subject remains in adherenceand/or compliance with the guidance and/or output of apparatus 4004.Apparatus 4004 may employ speech-to-text and/or text-to-speechalgorithms to at least allow audio deterrent 4024 to emit the wordsand/or phrases that language processing component may construct. Forexample, and without limitation, language processing component mayconstruct words and/or phrases that include directions to exit a subjectarea. As a further non-limiting example, language processing componentmay construct words and/or phrases that include strategies forde-escalating a violent and/or abusive behavior exhibited by subject4012. Additionally or alternatively, apparatus 4004 may identify speechand/or behaviors from subject 4012 as a function of the languageprocessing component generated words and/or phrases. Apparatus 4004 mayinput behavior classifiers, tool classifiers, and/or image classifiersto output subject-specific messages, wherein a subject specific messageare audio signals that convey a message and our direction to subject4012, as described in detail below. Apparatus 4004 may receive one ormore verbal statements outputted by the subject, wherein apparatus 4004may process and/or respond to the verbal states as a function of theverbal response, behavior classifier, tool classifier, and/or imageclassifier. Apparatus 4004 may respond to the subjects responsivebehaviors and repeat necessary iterations to at least maintain acommunicative conversation with subject 4012 until the speech and/orbehaviors exhibited by subject 4012 cease and/or subject 4012 exitssubject area 4016.

Still referring to FIG. 40 , language processing component may constructwords and/or phrases as a function of the attributes of an event, threatand/or status of subject area 4016. For example, and without limitation,language processing component may identify a particular individual usingimaging device 4008 and construct words and/or phrases as a function ofthe behavior classifier, image classifier, and threat level. Languageprocessor component may emit particular words and/or phrases that alertsurrounding subjects and/or individuals of a rapidly developing event.For example, and without limitation language processing component mayconstructs phrases such as “He's got a gun!” for an event of an activeshooter in a subject area.

Still referring to FIG. 40 , apparatus 4004 may be mounted and/ordeployed in a subject area. Security objectives may include exclusion ofunauthorized persons from subject area, prevention of unauthorizedpersons from entering a door in subject area, prevention of unauthorizedpersons from accessing an item to be protected 204, such as a valuableand/or dangerous item, protection of a person or object in subject areafrom harm, prevention of harm to apparatus 4004, or the like. Subjectarea may include, without limitation, a room, corridor, or otherinternal space, a fixed outdoor area such as a porch, patio, gazebo,stage, or the like, a geometrically defined area around a handheld ordrone-mounted device such as a cylindrical and/or spherical area definedby a given radius, or the like. A user may use a computing device suchas a control station, desktop computer, tablet, laptop, or the like toset bounds of subject area; alternatively or additionally, apparatus4004 may use automated detection using any imaging device 4008 or thelike to image and/or scan the subject area for processing to determinethe locations of walls, objects, animals, features, or other boundarydelineators to find potential boundaries, which a user may confirm froma user computing device.

Still referring to FIG. 40 , apparatus 4004 may use one or more imagingdevices 4008 to determine a baseline condition of subject area. Imagingdevice 4008 may map points within subject area to a coordinate system.Coordinate system may include x and y coordinates, which may correspondto axes on one or more focal planes of imaging device 4008,substantially horizontal and vertical axes, or the like, and a zcoordinate corresponding to depth and/or distance from imaging device4008. Depth may be determined using image analysis such as parallaxand/or analysis of relative sizes of objects, and/or using ToFrange-finding, and/or artificial intelligence inferred methods, and/orshape from motion or polarimetry, and/or computational vision methodsand algorithms. Optical, infrared, light radar devices, RF sensors,radar components, THz imagers, MMW imagers, polarized cameras,multispectral cameras, and/or hyperspectral cameras may each be used toregister boundaries of subject area and/or a geometric center thereof tocoordinate system. Objects within subject area may then be locatedwithin coordinate system to establish a baseline condition.

In an embodiment, and with continued reference to FIG. 40 , eachseparate camera, motion detector, or other component and/or device inapparatus 4008 may have an associated Cartesian and/or polar coordinatesystem. Apparatus 4008 may relate each such separate coordinate systemto a master coordinate system used by apparatus 4008, using withoutlimitation, one or more affine transformations, rotationaltransformations, perspective transformations, perspectivetransformations, and/or scaling operations, sometimes collectivelyreferred to as a “homography transformation” or “homography.”

With further reference to FIG. 40 , one or more subjects 4012 may bedetected via detection of changes to a baseline condition. Baselinecondition may be generated as a function of apparatus 4004 collectingdata in an ongoing manner. Apparatus 4004 may compare the monitored datato the model and/or baseline. Detection of changes from the baselinecondition may include detecting a louder overall level of audiblesounds, greater structural vibrations, unusual audio detections, and thelike thereof. A subject 4012 may include one or more persons who areauthorized to enter subject area and/or one or more persons who are notauthorized to enter subject area. Subjects 4012 may be identified,tracked, imaged, recorded, analyzed, hailed and/or subjected todeterrent actions by apparatus 4004 while within subject area, asdescribed in further detail below.

Referring now to FIG. 41 , an embodiment 4100 of a subject area forsound and/or speech localization is illustrated. Subject 4012 may beprojecting audio signals, such as speaking a language, wherein amicrophone 4104 may be positioned to receive audio signals. As used inthis disclosure a “microphone” is an instrument for converting soundwaves and/or audio signals into electrical energy vibrations that may beamplified, transmitted, and/or recorded. Microphone 4104 may include oneor more microphones positioned at one or more locations in a subjectarea. As a non-limiting example a first microphone 4104 a may be locatedin a far-left position, wherein a second microphone 4104 b may belocated to the right of first microphone 4104 a, but to the left of athird microphone 4104 c. As a further non-limiting example a fourthmicrophone 4104 d may be located to the far right of the room, whereinthe fourth microphone is located to the right of third microphone 4104c. Microphone 4104 may include one or more arrays of multi-directionalmicrophones. As used in this disclosure a “multi-directional microphone”is a microphone that may receive audio signals from a plurality ofangles and/or directions. For example, and without limitation, amulti-directional microphone may include a microphone that receivesaudio signals from directions within a 360° degree area. Microphone 4104may include a directionally determined sensitivity that varies withrespect to a reference vector 4108. As used in this disclosure a“reference vector” is a vector that is perpendicular to microphone 4104.First microphone 4104 a, second microphone 4104 b, third microphone 4104c, and/or 4104 d may be configured to have a reference vector. Forexample, and without limitation, first microphone 4104 a may beconfigured to include a first reference vector 4108 a. For example, andwithout limitation, second microphone 4104 b may be configured toinclude a second reference vector 4108 b. For example, and withoutlimitation, third microphone 4104 c may be configured to include a thirdreference vector 4108 c. For example, and without limitation, fourthmicrophone 4104 d may be configured to include a fourth reference vector4108 d.

Still referring to FIG. 41 , apparatus 4004 may utilize microphone 4104and/or reference vector 4108 to determine an angular difference 4112. Asused in this disclosure an “angular difference” is the angle between thedetected sound wave and/or audio signal by microphone 4104 and thereference vector 4108. As a non-limiting example, a first angulardifference 4112 a may include an angle of 36° from audio signals and/orsound waves emitted by subject 4012. As a further non, limiting example,a second angular difference 4112 b may include an angle of −0.1° fromaudio signals and/or sound waves emitted by subject 4012. As a furthernon, limiting example, a third angular difference 4112 c may include anangle of −28° from audio signals and/or sound waves emitted by subject4012. As a further non, limiting example, a fourth angular difference4112 d may include an angle of −78° from audio signals and/or soundwaves emitted by subject 4012. Additionally or alternatively, one ormore highly directional microphones may be utilized to determine adirection of origin of sound, wherein an “origin of sound”, as usedherein is the point at which an audio signal originates. As used in thisdisclosure a “highly directional microphone” is a microphone with aprimary axis that points in a single direction. As a non-limitingexample, a highly directional microphone may include an omnidirectionalmicrophone, bidirectional microphone, cardioid microphone, supercardioidmicrophone, hypercardioid microphone, subcardioid microphone,hemispherical microphone, boundary microphone, shotgun microphone, lobarmicrophone, and the like thereof. Highly directional microphones mayidentify the direction of the origin of sound as a function of aquantitative polar pattern graph. As used in this disclosure a“quantitative polar pattern graph” is a graph that represents themicrophones directional responses. Quantitative polar pattern graph maybe represented as a 2-dimensional plane around the primary axis of thehighly directional microphone. The primary axis of the highlydirectional microphone is shown on the graph at 0°, and the polar graphextends to 360° clockwise. The outer circle is denoted by 0 dB and theon-axis response of the highly directional microphone always reachesthis outer circle at 0 db. Quantitative polar pattern graph may includea polar response line that is drawn on the polar graph that representsthe angle at which a sound and/or audio signal is received along withthe intensity values. Apparatus 4004 may determine the location ofsubject 4012 as a function of the emitted sound waves and/or audiosignals that are detected by the array of multi-directional microphonesand/or highly directional microphones. These devices and/or techniquesmay be used to determine the direction of the sound and/or the intensityof the sound, which can be linked to a subject detected by imagingdevice 4008 and/or other devices.

In an embodiment and still referring to FIG. 41 , apparatus 4004 maydetermine a specific location of the sound being emitted by subject 4012and combine information received by imaging device 4008. The combinationof the sound and/or acoustic information from microphone 4104 and theimaging information from imaging device 4008 may allow apparatus 4004 todetermine a particular subject in a room with n number of subjects. Forexample, and without limitation, speech associated with abusive languagemay be determined in a particular location of a subject area, whereinimaging device 4044 may characterize a particular subject that isemitting the words as a function of both the speech localization and theimaging information. Microphones 4104 and/or audio deterrent 4024 maythen be directed to that particular subject exhibiting the aggressivebehavior such that the remained of the subjects are not affected by theaudio deterrent signal.

In an embodiment and still referring to FIG. 41 , apparatus 4004 mayoutput a subject-specific message. As used in this disclosure an“subject-specific message” is an audio signal to convey a message andour direction to subject 4012. For example, and without limitation,apparatus 4004 may output a subject-specific message such as “you withthe orange scarf, step away from the counter!” As a further non-limitingexample, apparatus 4004 may output a subject specific message such as“sir in the brown trousers, you are not authorized in this subject area,please leave.” As a further non limiting example, apparatus 4004 mayoutput a subject specific message such as “you in the green blouse andred shoes, if you do not cease and desist a deterrent action will beemitted.” Additionally or alternatively, apparatus 4004 may outputsubject-specific messages and listen to the subject's response. Forexample, and without limitation, a subject may response “I am sorry, Iwill leave this subject area.” As a further non-limiting example, asubject may respond “Shut up, I will not listen to you!” As a furthernon-limiting example, a subject may respond with no verbalcommunication, rather only a gesture and/or behavior. Apparatus mayrespond to the subject's response in a manner similar to thesubject-specific message. For example, and without limitation, apparatus4004 may output a response such as “Thank you for cooperating, nofurther action will be taken”. As a further non-limiting exampleapparatus 4004 may output a response such as “You have not complied withthe request, a deterrent will be emitted in 3 seconds.” As a furthernon-limiting example apparatus 4004 may respond to an obscene gesturedisplayed by the subject in response to subject-specific message byoutputting an audio signal such as “please do not conduct that behavioragain or further action will be taken.” Apparatus 4004 may outputsubject-specific messages, receive subject audio and/or visualresponses, and respond in an iterative manner. For example, apparatus4004 may output a first subject-specific message, receive a firstsubject response, output a first apparatus response, receive a secondsubject response, and output a second apparatus response.

In an embodiment, a multimodal deterrent apparatus with an internalwatchdog system includes a deterrent suite, the deterrent suit includinga first deterrent component including a first deterrent and a firstoutput reduction element and a second deterrent component including asecond deterrent distinct from the first deterrent and a second outputreduction element, and an internal watchdog system, the internalwatchdog system including a detector component configured to detect afirst parameter of the first deterrent component and a second parameterof the second deterrent component and a control component configured tocompare each of the first parameter and the second parameter to areference value and activate at least one of the first output reductionelement and the second output reduction element as a function of thecomparing.

First deterrent component may include a directed light deterrent. Firstoutput reduction element may include a shutter. First output reductionelement may include an optical modulator. First parameter may includeirradiance generated by the directed light deterrent. Second deterrentcomponent may include an audio deterrent. Second parameter may include ameasure of acoustic intensity. At least one of the first parameter andthe second parameter may include an electrical parameter. Electricalparameter may include a voltage level. Electrical parameter may includea current. At least one of the first parameter and the second parametermay include a temperature. At least one of the first parameter and thesecond parameter may include a cumulative energy value. Cumulativeenergy value may include a per-engagement value. Control component maybe further configured to determine that an engagement has terminated andreset the per-engagement value. At least one of the first parameter andthe second parameter may include a location-dependent parameter. Atleast one of the first parameter and the second parameter may include adistance-dependent parameter. At least one of first output reductionelement and second output reduction element may include a powerregulation control element. Control component may be configured toreceive the reference value from a processor. Internal watchdog systemmay be configured to transmit at least a sensor output to the processor,and wherein receiving the reference value further includes receiving thereference value as a function of the transmitted sensor output. Controlcomponent may be configured to transmit an indication of outputreduction activation to a processor.

In an embodiment, an autonomous safety system for a deterrent apparatusincludes a processor configured to detect, using at least a detectioncomponent communicatively connected to at least a deterrent of adeterrent apparatus, at least a deterrent parameter, compare the atleast a deterrent parameter to a safety threshold, determine acorrective action as a function of the comparison, and initiate thecorrective action.

In an embodiment, parameter may include irradiance generated by adirected light deterrent, a measure of acoustic intensity, atemperature, and/or an electrical parameter; electrical parameter mayinclude a voltage level and/or a current. At least one of the firstparameter and the second parameter may include a cumulative energyvalue. Cumulative energy value may include a per-engagement value.Processor may be further configured to transmit the corrective action tothe apparatus.

In an embodiment, a method of operating an autonomous safety system fora deterrent apparatus includes detecting, by a processor and using atleast a detection component communicatively connected to at least adeterrent of a deterrent apparatus, at least a deterrent parameter,comparing, by the processor, the at least a deterrent parameter to asafety threshold, determining, by the processor, a corrective action asa function of the comparison, and initiating, by the processor, thecorrective action.

In an embodiment, an automated threat detection and deterrence apparatusis configured to identify a behavior of an individual as a function ofat least a datum regarding the individual, determine a candidatedeterrent space as a function of the behavior and at least a deterrentcomponent, receive a target impact level function of the candidatedeterrent space, wherein the target impact level function is generatedas a function of an impact machine-learning process and at least animpact training set, select a deterrent from the candidate deterrentspace that minimizes the target impact level function, and initiate theselected deterrent.

In an embodiment, identifying the behavior may include receiving arecognition element. Identifying the behavior may include identifyingthe behavior as a function of the at least a datum, and a behaviormachine-learning model. Identifying the behavior may include receiving apsychological indicator. Receiving a target impact level function mayinclude receiving from a remote device the target impact level function.Apparatus may be further configured to initiate the selected deterrentand receive at least a feedback input using at least a sensor. Receivingthe feedback input may include obtaining a first behavioral responseassociated with the selected deterrent and determining a correctedimpact level function as a function of the first behavioral response.Minimizing the target level impact may include generating a behaviormodification function and determining a behavior modification as afunction of the candidate deterrent space and the behavior modificationfunction. Generating the behavior modification function may includegenerating the behavior modification function as a function of theidentified behavior and a modification machine-learning model.Minimizing the target level impact function may include minimizing withrespect to at least a deterrent output constraint.

In an embodiment, a method of an automated threat detection anddeterrence apparatus includes identifying, by an apparatus, a behaviorof an individual as a function of at least a datum regarding theindividual, determining, by the apparatus, a candidate deterrent spaceas a function of the behavior, generating, by the apparatus, a targetimpact level function of the candidate deterrent space, whereingenerating also includes determining at least a deterrent impact,determining at least a behavior response, and generating a target impactlevel function as a function of the at least deterrent impact, at leastbehavior response, and candidate deterrent space, selecting, by theapparatus, a deterrent from the candidate deterrent space that minimizesthe target impact level function, and initiating, by the apparatus, theselected deterrent.

In an embodiment, an automated threat detection and deterrence apparatuswith position dependent deterrence is configured to identify a behaviorof an individual as a function of at least a datum regarding theindividual, determine at least a deterrent that impacts the behavior,identify at least a spatiotemporal element related to the individual andthe at least deterrent, generate a safety modifier as a function of thespatiotemporal element, wherein generating includes identifying adistance parameter as a function of the spatiotemporal element,determining a velocity parameter as a function of the spatiotemporalelement, and generating the safety modifier as a function of thedistance parameter and velocity parameter, modify the at least adeterrent as a function of the safety modifier, and initiate a modifieddeterrent.

In an embodiment, identifying the behavior may include receiving arecognition element. Identifying the behavior may include receiving atleast a user action from the plurality of sensors, identifying an atleast accepted standard, and identifying a behavior as a function of theuser action and at least accepted standard. Determining the at least adeterrent may include identifying candidate deterrents from a deterrentdatabase, determining a behavior impact value, and selecting at least adeterrent that improves the behavior impact value. Spatiotemporalelement may be identified from a sensor of a plurality of sensors.Spatiotemporal element may include at least a movement of the individualat a specific time that relates to a deterrent location. Generating thesafety modifier may include determining a deterrent impact andgenerating the safety modifier as a function of the at leastspatiotemporal element and the deterrent impact. Generating the safetymodifier may include generating the safety modifier as a function of thespatiotemporal element and a safety machine-learning model. Modifying adeterrent may include determining a deterrent element of the at least adeterrent that differs from the safety modifier and modifying thedeterrent as a function of the deterrent element. Deterrent element maycontrol the deterrent output.

In an embodiment, a method of position-dependent deterrence includesidentifying, by an apparatus, a behavior of an individual as a functionof at least a datum regarding the individual, determining, by theapparatus, at least a deterrent that impacts the behavior, identifying,by the apparatus, at least a spatiotemporal element related to theindividual and the at least deterrent, generating, by the apparatus, asafety modifier as a function of the spatiotemporal element, whereingenerating includes identifying a distance parameter as a function ofthe spatiotemporal element, determining a velocity parameter as afunction of the spatiotemporal element, and generating the safetymodifier as a function of the distance parameter and velocity parameter,modifying, by the apparatus, the at least a deterrent as a function ofthe safety modifier, and initiating, by the apparatus, a modifieddeterrent.

In an embodiment, an apparatus for altering an individual behavior isconfigured to receive a plurality of sensor data, identify a behavior ofa first individual of a plurality of individuals as a function of theplurality of sensor data, determine as a function of the individualbehavior at least a behavior element, generate a behavioral remedy thatalters at least the behavior element, wherein generating includesidentifying at least a deterrent of a plurality of deterrents,determining a collateral parameter related to the deterrent, andgenerating a behavioral remedy as a function of the collateral parameterand a congestion variable, and administer as a function of thebehavioral remedy a deterrent that alters the behavior of the firstindividual.

In an embodiment, receiving a plurality of sensor data includes mayinclude receiving a recognition element from a plurality of sensors,obtaining an identification element of an individual from anidentification database, and relating the recognition element to theidentification element using a recognition model. Identifying thebehavior of the first individual may include identifying at least aphysiological action as a function of the plurality of sensor data andidentifying the behavior as a function of the at least a physiologicalaction. Identifying the behavior may include identifying the behavior asa function of the physiological action, at least an accepted standard,and a behavior model. At least an accepted standard may be obtained froman ethical construct. Determining a behavior element may includereceiving an assemblage comparator and determining a behavior element asa function of the assemblage comparator and an assemblage model.Assemblage comparator may include a measurable reference of behaviorsassociated with the assemblage. Apparatus may be further configured togenerate the congestion variable, and generating the congestion variablemay include receiving a spatiotemporal element of an individual of aplurality of individuals in the assemblage, identifying a socialdistance metric relating to the at least an individual, and determininga congestion variable as a function of the social distance metric.Administering the deterrent may include identifying the at least adeterrent as a function of the plurality of deterrents and a linearprogramming algorithm. Identifying the deterrent may include creating adistance metric from the at least plurality of candidate deterrents tothe behavior remedy and determining the deterrent that at leastminimizes the distance metric.

In an embodiment, a method of an apparatus for altering an individualbehavior includes receiving, by an apparatus, a plurality of sensordata, identifying, by the apparatus, a behavior of a first individual ofa plurality of individuals as a function of the plurality of sensordata, determining, by the apparatus, as a function of the individualbehavior at least a behavior element, generating, by the apparatus, abehavioral remedy that alters at least the behavior element, whereingenerating includes identifying at least a deterrent of a plurality ofdeterrents, determining a collateral parameter related to the deterrent,generating a behavioral remedy as a function of the collateral parameterand a congestion variable, and administering, by the apparatus, as afunction of the behavioral remedy a deterrent that alters the behaviorof the first individual.

In an embodiment, an automated threat detection and deterrence apparatusfor commanding a deterrent as a function of importance level includes animaging device configured to detect a subject in a subject area, adeterrent component, wherein the deterrent component includes a firstdeterrent mode and a second deterrent mode, the deterrent component isconfigured to perform a first deterrent action on the subject when inthe first mode, the deterrent component is configured to perform asecond deterrent action on the subject when in the second mode, and thefirst deterrent action is distinct from the second deterrent action, anda control circuit communicatively connected to the imaging device andthe deterrent component, wherein the processor is configured to identifyan object to be protected, determine an importance level of the objectbeing protected, select, as a function of the importance level, a modeof the first deterrent action and the second deterrent mode, detect thesubject as a function of the detection of the subject, and command thedeterrent component to perform an action of the first deterrent actionand the second deterrent action as a function of the importance level.

In an embodiment, identifying the object being protected may includereceiving a user input. In an embodiment, identifying the object mayinclude obtaining at least a sensor datum. Identifying at least anobject being protected may include determining at least a recognitionelement from the sensor datum and identifying at least an object as afunction of the at recognition element. Determining an importance levelmay include receiving an originality metric as a function of the objectbeing protected, determining an expendable element as a function of theoriginality vector, and generating an importance level as a function ofthe expendable element and at least an importance model. Determining animportance level may include determining an at least support metric.Determining the at least support metric may include receiving at least asupport advisor from a support database, identifying at least ageolocation of the at least support advisor, and determining the atleast support metric as a function of the at least geolocation.Determining an importance level may include receiving at least anupdated importance model from a remote device, replacing the importancemodel with the updated importance model, and generating the importancelevel as a function of the expendable element and the updated importancemodel. Determining the at least deterrent may include receivingcandidate deterrents from a deterrent database, identifying an impactparameter that relates to the importance level, and selecting adeterrent as a function of the impact parameter. Impact parameter mayinclude at least a parameter of collateral impact.

In an embodiment, a method of commanding a deterrent as a function ofimportance level includes detecting, by a processor communicativelyconnected to an imaging device and a deterrent component, a subject as afunction of an imaging device, identifying, by the processor, an objectto be protected, determining, by the processor, an importance levelassociated with the object, selecting, by the processor, a mode of afirst deterrent mode and a second deterrent mode as a function of theimportance level, and commanding, by the processor, the deterrentcomponent to perform an action of a first deterrent action and a seconddeterrent action as a function of the importance level, wherein thefirst deterrent action is distinct from the second deterrent action.

In an embodiment, a system for transmitting a notification includes anautomated threat deterrence apparatus configured to obtain a pluralityof sensory inputs from a recognition sensor, identify an individual of aplurality of individuals as a function of the plurality of sensoryinputs, determine at least a behavior of the individual as a function ofthe plurality of sensory inputs, wherein determining further comprisesidentifying at least a behavioral element and determining at least abehavior as a function of the behavioral elements and the plurality ofsensory inputs, generate a behavior value as a function of the at leasta behavior, determine a threshold constraint, and transmit anotification as a function of the behavior value and thresholdconstraint.

In an embodiment, at least a behavior may include a plurality ofbehaviors, and determining the behavior value may include generating abehavior grouping element, arranging the plurality of behaviorsaccording to a behavior grouping element, and determining the behaviorvalue as a function of the behavior grouping element. Determining thebehavior grouping element as a function of the determined behavior mayinclude receiving a behavior grouping element, wherein the behaviorgrouping element is generated as a function of a groupingmachine-learning model and at least a receiving training set. Generatinga behavior value may include identifying a vector outcome as a functionof the behavior using a vector model, wherein a vector model iscomprised of a many-valued logic algorithm. A vector outcome may includereceiving an actual outcome of an individual from at least a recognitionsensor, correlating the actual outcome to a psyche databank, andgenerating a vector outcome using statistical inference. Determining aconstraint threshold may include grouping at least a behavior value of aplurality of behavior values to a threshold qualifier. Thresholdqualifier may include heuristic algorithms. Threshold qualifier mayinclude fuzzy logic algorithms. Notification may include a behavior ofthe at least a behavior that at least violates a threshold constraint.Violation may include receiving a behavior value of a behavior,receiving a threshold constraint relating to the behavior, anddetermining a violation relating the behavior value and the thresholdconstraint as a function of a behavior algorithm.

In an embodiment, a method of transmitting a notification includesobtaining, by an apparatus, a plurality of sensory inputs from arecognition sensor, identifying, by the apparatus, an individual of aplurality of individuals as a function of the plurality of sensoryinputs, determining, by the apparatus, at least a behavior of theindividual as a function of the plurality of sensory inputs, whereindetermining includes identifying at least a behavioral element anddetermining at least a behavior as a function of the behavioral elementsand the plurality of sensory inputs, generating, by the apparatus, abehavior value as a function of the at least a behavior, determining, bythe apparatus, a threshold constraint, and transmitting, by theapparatus, a notification as a function of the behavior value andthreshold constraint.

In an embodiment, an automated threat detection and deterrence apparatusincludes an imaging device configured to detect a subject in a subjectarea, a threat response component including an audio deterrent, whereinthe audio deterrent is configured to perform a first deterrent action onthe subject, the first deterrent action corresponding to a first threatlevel, the audio deterrent is configured to perform a second deterrentaction on the subject, the second deterrent action corresponding to asecond threat level, and the first deterrent action is distinct from thesecond deterrent action and a processor communicatively connected to theimaging device and the threat response component, wherein the processoris configured to identify the subject as a function of the detection ofthe subject, determine a threat level, of the first threat level and thesecond threat level, associated with the subject, and command the audiodeterrent to perform an action of the first deterrent action and thesecond deterrent action as a function of the determined threat level.

In an embodiment, the imaging device may include an optical camera, aninfrared camera, and/or a light radar component. Processor may befurther configured to determine a distance from the apparatus to thesubject using the light radar component. Audio deterrent may include adirected sound source. Processor may be configured to determine apersonal identity of the subject as a function of the detection of thesubject. Processor may be configured to determine an authorization levelof the subject as a function of the personal identity. Processor may beconfigured to determine a distance to the subject and determine thethreat level as function of the distance. Processor may be configured todetermine a time of day and determine the threat level as function ofthe time of day. Processor may be configured to detect a behavior of thesubject and determine the threat level as function of behavior.Processor may be configured to identify an object in possession of thesubject and determine the threat level as a function of the object.Processor may be configured to detect a speech of the subject anddetermine the threat level as a function of the speech. Detecting thespeech may include determining a semantic tone and detecting the speechas a function of the semantic tone.

In an embodiment, a method of automated threat detection and deterrenceincludes identifying, by a processor communicatively connected to animaging device and a threat response component including an audiodeterrent, a subject as a function of a detection of the subject by theimaging device, determining, by the processor, a threat level associatedwith the subject, selecting, by the processor, a mode of a firstdeterrent action and a second deterrent action as a function of thethreat level, and commanding, by the processor, the audio deterrent toperform an action of a first deterrent action and a second deterrentaction as a function of the mode, wherein the first deterrent action isdistinct from the second deterrent action.

It is to be noted that any one or more of the aspects and embodimentsdescribed herein may be conveniently implemented using one or moremachines (e.g., one or more computing devices that are utilized as auser computing device for an electronic document, one or more serverdevices, such as a document server, etc.) programmed according to theteachings of the present specification, as will be apparent to those ofordinary skill in the computer art. Appropriate software coding canreadily be prepared by skilled programmers based on the teachings of thepresent disclosure, as will be apparent to those of ordinary skill inthe software art. Aspects and implementations discussed above employingsoftware and/or software modules may also include appropriate hardwarefor assisting in the implementation of the machine executableinstructions of the software and/or software module.

Such software may be a computer program product that employs amachine-readable storage medium. A machine-readable storage medium maybe any medium that is capable of storing and/or encoding a sequence ofinstructions for execution by a machine (e.g., a computing device) andthat causes the machine to perform any one of the methodologies and/orembodiments described herein. Examples of a machine-readable storagemedium include, but are not limited to, a magnetic disk, an optical disc(e.g., CD, CD-R, DVD, DVD-R, etc.), a magneto-optical disk, a read-onlymemory “ROM” device, a random-access memory “RAM” device, a magneticcard, an optical card, a solid-state memory device, an EPROM, an EEPROM,and any combinations thereof. A machine-readable medium, as used herein,is intended to include a single medium as well as a collection ofphysically separate media, such as, for example, a collection of compactdiscs or one or more hard disk drives in combination with a computermemory. As used herein, a machine-readable storage medium does notinclude transitory forms of signal transmission.

Such software may also include information (e.g., data) carried as adata signal on a data carrier, such as a carrier wave. For example,machine-executable information may be included as a data-carrying signalembodied in a data carrier in which the signal encodes a sequence ofinstruction, or portion thereof, for execution by a machine (e.g., acomputing device) and any related information (e.g., data structures anddata) that causes the machine to perform any one of the methodologiesand/or embodiments described herein.

Examples of a computing device include, but are not limited to, anelectronic book reading device, a computer workstation, a terminalcomputer, a server computer, a handheld device (e.g., a tablet computer,a smartphone, etc.), a web appliance, a network router, a networkswitch, a network bridge, any machine capable of executing a sequence ofinstructions that specify an action to be taken by that machine, and anycombinations thereof. In one example, a computing device may includeand/or be included in a kiosk.

FIG. 42 shows a diagrammatic representation of one embodiment of acomputing device in the exemplary form of a computer system 4200 withinwhich a set of instructions for causing a control system to perform anyone or more of the aspects and/or methodologies of the presentdisclosure may be executed. It is also contemplated that multiplecomputing devices may be utilized to implement a specially configuredset of instructions for causing one or more of the devices to performany one or more of the aspects and/or methodologies of the presentdisclosure. Computer system 4200 includes a processor 4204 and a memory4208 that communicate with each other, and with other components, via abus 4212. Bus 4212 may include any of several types of bus structuresincluding, but not limited to, a memory bus, a memory controller, aperipheral bus, a local bus, and any combinations thereof, using any ofa variety of bus architectures.

Processor 4204 may include any suitable processor, such as withoutlimitation a processor incorporating logical circuitry for performingarithmetic and logical operations, such as an arithmetic and logic unit(ALU), which may be regulated with a state machine and directed byoperational inputs from memory and/or sensors; processor 4204 may beorganized according to Von Neumann and/or Harvard architecture as anon-limiting example. Processor 4204 may include, incorporate, and/or beincorporated in, without limitation, a microcontroller, microprocessor,digital signal processor (DSP), Field Programmable Gate Array (FPGA),Complex Programmable Logic Device (CPLD), Graphical Processing Unit(GPU), general purpose GPU, Tensor Processing Unit (TPU), analog ormixed signal processor, Trusted Platform Module (TPM), a floating-pointunit (FPU), and/or system on a chip (SoC).

Memory 4208 may include various components (e.g., machine-readablemedia) including, but not limited to, a random-access memory component,a read only component, and any combinations thereof. In one example, abasic input/output system 4216 (BIOS), including basic routines thathelp to transfer information between elements within computer system4200, such as during start-up, may be stored in memory 4208. Memory 4208may also include (e.g., stored on one or more machine-readable media)instructions (e.g., software) 4220 embodying any one or more of theaspects and/or methodologies of the present disclosure. In anotherexample, memory 4208 may further include any number of program modulesincluding, but not limited to, an operating system, one or moreapplication programs, other program modules, program data, and anycombinations thereof.

Computer system 4200 may also include a storage device 4224. Examples ofa storage device (e.g., storage device 4224) include, but are notlimited to, a hard disk drive, a magnetic disk drive, an optical discdrive in combination with an optical medium, a solid-state memorydevice, and any combinations thereof. Storage device 4224 may beconnected to bus 4212 by an appropriate interface (not shown). Exampleinterfaces include, but are not limited to, SCSI, advanced technologyattachment (ATA), serial ATA, universal serial bus (USB), IEEE 1394(FIREWIRE), and any combinations thereof. In one example, storage device4224 (or one or more components thereof) may be removably interfacedwith computer system 4200 (e.g., via an external port connector (notshown)). Particularly, storage device 4224 and an associatedmachine-readable medium 4228 may provide nonvolatile and/or volatilestorage of machine-readable instructions, data structures, programmodules, and/or other data for computer system 4200. In one example,software 4220 may reside, completely or partially, withinmachine-readable medium 4228. In another example, software 4220 mayreside, completely or partially, within processor 4204.

Computer system 4200 may also include an input device 4232. In oneexample, a user of computer system 4200 may enter commands and/or otherinformation into computer system 4200 via input device 4232. Examples ofan input device 4232 include, but are not limited to, an alpha-numericinput device (e.g., a keyboard), a pointing device, a joystick, agamepad, an audio input device (e.g., a microphone, a voice responsesystem, etc.), a cursor control device (e.g., a mouse), a touchpad, anoptical scanner, a video capture device (e.g., a still camera, a videocamera), a touchscreen, and any combinations thereof. Input device 4232may be interfaced to bus 4212 via any of a variety of interfaces (notshown) including, but not limited to, a serial interface, a parallelinterface, a game port, a USB interface, a FIREWIRE interface, a directinterface to bus 4212, and any combinations thereof. Input device 4232may include a touch screen interface that may be a part of or separatefrom display 4236, discussed further below. Input device 4232 may beutilized as a user selection device for selecting one or more graphicalrepresentations in a graphical interface as described above.

A user may also input commands and/or other information to computersystem 4200 via storage device 4224 (e.g., a removable disk drive, aflash drive, etc.) and/or network interface device 4240. A networkinterface device, such as network interface device 4240, may be utilizedfor connecting computer system 4200 to one or more of a variety ofnetworks, such as network 4244, and one or more remote devices 4248connected thereto. Examples of a network interface device include, butare not limited to, a network interface card (e.g., a mobile networkinterface card, a LAN card), a modem, and any combination thereof.Examples of a network include, but are not limited to, a wide areanetwork (e.g., the Internet, an enterprise network), a local areanetwork (e.g., a network associated with an office, a building, a campusor other relatively small geographic space), a telephone network, a datanetwork associated with a telephone/voice provider (e.g., a mobilecommunications provider data and/or voice network), a direct connectionbetween two computing devices, and any combinations thereof. A network,such as network 4244, may employ a wired and/or a wireless mode ofcommunication. In general, any network topology may be used.

Information (e.g., data, software 4220, etc.) may be communicated toand/or from computer system 4200 via network interface device 4240.

Computer system 4200 may further include a video display adapter 4252for communicating a displayable image to a display device, such asdisplay device 4236. Examples of a display device include, but are notlimited to, a liquid crystal display (LCD), a cathode ray tube (CRT), aplasma display, a light emitting diode (LED) display, and anycombinations thereof. Display adapter 4252 and display device 4236 maybe utilized in combination with processor 4204 to provide graphicalrepresentations of aspects of the present disclosure. In addition to adisplay device, computer system 4200 may include one or more otherperipheral output devices including, but not limited to, an audiospeaker, a printer, and any combinations thereof. Such peripheral outputdevices may be connected to bus 4212 via a peripheral interface 4256.Examples of a peripheral interface include, but are not limited to, aserial port, a USB connection, a FIREWIRE connection, a parallelconnection, and any combinations thereof.

The foregoing has been a detailed description of illustrativeembodiments of the invention. Various modifications and additions can bemade without departing from the spirit and scope of this invention.Features of each of the various embodiments described above may becombined with features of other described embodiments as appropriate inorder to provide a multiplicity of feature combinations in associatednew embodiments. Furthermore, while the foregoing describes a number ofseparate embodiments, what has been described herein is merelyillustrative of the application of the principles of the presentinvention. Additionally, although particular methods herein may beillustrated and/or described as being performed in a specific order, theordering is highly variable within ordinary skill to achieve methods,systems, and software according to the present disclosure. Accordingly,this description is meant to be taken only by way of example, and not tootherwise limit the scope of this invention.

Exemplary embodiments have been disclosed above and illustrated in theaccompanying drawings. It will be understood by those skilled in the artthat various changes, omissions and additions may be made to that whichis specifically disclosed herein without departing from the spirit andscope of the present invention.

1-23. (canceled) 24: An automated detection and tracking systemcomprising: a detection device configured to persistently detect andpersistently track an object; a processor communicatively connected tothe detection device wherein the processor is configured to: instructthe detection device to persistently detect and persistently track theobject; instruct a machine learning model to classify the object basedon information obtained from the persistent detection and persistenttracking; and obtain a classification of the object as an output of themachine leaning model. 25: The automated detection and tracking systemof claim 24, wherein the processor is configured to instruct thedetection device to persistently detect and persistently track theobject based on the classification. 26: The automated detection andtracking system of claim 24, further comprising: a deterrent componentcommunicatively connected to the processor, wherein the processor isconfigured to instruct the deterrent component to persistently perform afirst deterrent action to the object based on the classification. 27:The automated detection and tracking system of claim 26, wherein theprocessor is configured to instruct the deterrent component topersistently performs a second deterrent action to the object based onupdated information obtained from the detection device. 28: Theautomated detection and tracking system of claim 26, wherein theprocessor is configured to obtain a distance from the deterrentcomponent to the object and to instruct the deterrent component topersistently perform a second deterrent action to the object based onupdated information, obtained from the detection device and the distancefrom the deterrent component to the object. 29: The automated detectionand tracking system of claim 26, wherein the first deterrent action isan audio warning, and the audio warning is designed to alert the objectthat the object is on protected grounds. 30: The automated detection andtracking system of claim 29, wherein, after the first deterrent actionis performed, if the system determines that the object continues to beaggressive based on detected behavioral characteristics, then theprocessor is configured to instruct the deterrent component to perform asecond deterrent action to the object, wherein the second deterrentaction is at least one of an audio warning with increased decibel level,increased repetition rate, or varied acoustic sounds or messages thanthe audio warning of the first deterrent action. 31: The automateddetection and tracking system of claim 26, wherein the objectclassification determines the presence of eyewear on the object. 32: Theautomated detection and tracking system of claim 24, wherein thedetection device comprises an imaging device configured to detect lightfrom naturally occurring photons reflected by the object. 33: Theautomated detection and tracking system of claim 26, further comprisingan infrared light emitter, wherein the detection device comprises animaging device configured to detect infrared light retroflected by theobject. 34: The automated detection and tracking system of claim 33,wherein said system classifies the object as incorporating a lens. 35:The automated detection and tracking system of claim 34, wherein thefirst deterrent action performed on the object is continuously aimed atthe lens and designed to blind or obfuscate a sensor of the object. 36:The automated detection and tracking system of claim 35, where the lensis a component of a camera positioned on an uncrewed vehicle. 37: Theautomated detection and tracking system of claim 33, wherein if saidsystem determines the object incorporates a lens, then said systemdetermines a location of an eyeball of the object based on informationfrom the retroreflected light, and the first deterrent action performedon the object is continuously aimed at the eyeball to interdict theobject's vision; wherein the object is a human subject. 38: Theautomated detection and tracking system of claim 26, wherein the objectis a human subject. 39: The automated detection and tracking system ofclaim 26, wherein the first deterrent action is a light deterrentdirected to a retina of the object, wherein the light deterrent isstrobed between 8 Hertz and 25 Hertz. 40: The automated detection andtracking system of claim 26, wherein the first deterrent action is alight deterrent directed to the object's left eye and right eye, whereinthe light deterrent is configured to direct a first wavelength to theleft eye and a second wavelength to the right eye, wherein the firstcolor is different from the second color. 41: The automated detectionand tracking system of claim 27, wherein the first deterrent action orthe second deterrent action induces a visual impairment including atleast one of a glare effect, afterimage effect, or saturation effectdirected to the object. 42: The automated detection and tracking systemof claim 27, wherein the first deterrent action or the second deterrentaction induces dermal agitation to the object by at least one ofmicrowave, photonic deterrent, or laser deterrent. 43: The automateddetection and tracking system of claim 39, wherein the processor isconfigured to: detect a behavioral characteristic of the object, whereinthe behavioral characteristic is a compromised central nervous system ofthe object; and instruct the deterrent component to vary the firstdeterrent action based on at least one of strobe frequency, strobe luxintensity, or strobe wavelength.
 44. The automated detection andtracking system of claim 27, wherein the processor is configured toinstruct the deterrent component to vary the first deterrent action orthe second deterrent action based on a detected behavior response of theobject. 45: The automated detection and tracking system of claim 38,wherein the first deterrent action is designed to cause the humansubject to change the human subject's motivation, by increasingcognitive load on the human subject.
 46. The automated detection andtracking system of claim 24, wherein the detection device is configuredto determine a location of the object based on data received from pings,wherein the pings are GPS, wireless, or Bluetooth signals. 47: Theautomated detection and tracking system of claim 24, further comprising:a deterrent component communicatively connected to the processor andconfigured to persistently perform one or more deterrent actions to theobject, wherein the processor is configured to instruct the deterrentcomponent to perform the one or more deterrent actions to the objectbased on the classification, said apparatus configured to detect acountermeasure worn, mounted, attached, coupled, or used by the object,and wherein the countermeasure is configured to counteract the one ormore deterrent actions. 48: The automated detection and tracking systemof claim 47, wherein said apparatus is configured to select a deterrentaction designed to bypass or overcome the detected countermeasure by theobject. 49: The automated detection and tracking system of claim 48,wherein the selection is performed using a machine-learning process,which is trained using training examples correlating countermeasures tosuccessful deterrents previously deployed against such countermeasures.50: The automated detection and tracking system of claim 48, wherein theselection is performed using a lazy learning machine-learning process,which is trained using training data input by a user.
 51. The automateddetection and tracking system of claim 47, wherein said system isconfigured to detect countermeasures by the object usingmachine-learning and anatomical or anthropomorphic landmark locationprocesses. 52: The automated detection and tracking system of claim 24,further comprising an emitter configured to emit an emission; andwherein the detection device is configured to detect the object based onthe emission. 53: An automated threat detection and deterrence apparatuscomprising: a deterrent component; a detection device configured topersistently detect a subject; a processor communicatively connected tothe deterrent component and the detection device wherein the processoris configured to: instruct the detection device to identify the subject;determine whether the subject is a threat; persistently track thesubject; instruct the deterrent component to perform a first deterrentaction on the subject; track a total load of deterrent action receivedby the subject; instruct the deterrent component to perform a seconddeterrent action on the subject based on the total load received by thesubject; wherein the processor limits the total load received by thesubject to under a threshold.